Reputazione, sirena del presente

Ricco, famoso, pluripremiato, Orlando Figes aveva tutto dalla vita. Figlio della femminista Eva Figes, sposato a un’avvocata conosciuta, il più celebre storico britannico dell’Unione Sovietica poteva dormire sonni tranquilli. E invece, la notte, stava sveglio a scrivere su Amazon.co.uk recensioni velenose contro i libri dei suoi colleghi sovietologi per rovinare la loro reputazione. Autore di best-sellers come Sospetto e silenzio. Vite private nella Russia di Stalin (Mondadori, 2009) e La danza di Natasha. Storia della cultura russa (Einaudi, 2004), Figes era apprezzato sia dal grande pubblico sia dai colleghi specialisti, una reputazione rara, da difendere a caro prezzo, perché normalmente chi è amato dai molti è odiato dai pari. Eppure, per la sete di gloria, ha perduto tutto.

All’ennesima stroncatura online, il suo rivale, Robert Service, professore di storia a Oxford, comincia a insospettirsi. Le recensioni, che definiscono il suo ultimo libro, Comrades, “orrendo” e “curiosamente noioso”, provengono tutte da un recensore anonimo, che si firma historian. Da bravo storico, Service si lancia in una ricostruzione filologica dello stile dei messaggi, e, quando inizia ad accumulare prove, ne parla ad altri colleghi anch’essi colpiti dalle stroncature misteriose, e scrive ad Amazon per domandare l’indirizzo IP del computer da cui provengono i messaggi. Intanto, Rachel Polonsky, che aveva recensito negativamente qualche anno prima un libro di Figes sul Times Literary Supplement, riceve la seguente recensione del suo ultimo lavoro, Molotov’s Magic Lantern, dallo stesso misterioso historian: “E’ uno di quei libri riguardo al quale la prima domanda che viene in mente è perché sia stato scritto”. La Polonsky e Service proseguono insieme l’inchiesta: in effetti, basta un click sul profilo di historian per vedere che è uno pseudonimo legato al conto orlando-birkbeck, un bell’atto mancato per qualcuno che vuole distruggere i suoi nemici. Polonsky salva tutte le recensioni di historian, tra le quali anche un’invettiva contro Kate Summerscale, che nel 2008 aveva soffiato a Figes un premio letterario importante: “A cosa stavano pensando i giurati del Samuel Johnson quando hanno dato deciso di premiare questo libro?” In verità, historian non si limita a stroncare gli avversari: ama anche scrivere recensioni appassionate, solo però dei libri di Figes. Di Sospetto e Silenzio, infatti, scrive: “Meravigliosamente scritto, lascia il lettore stupito, travolto eppure più lucido di prima. Un regalo per tutti noi”.

Accusato, Figes contrattacca, nega tutto, dice agli avvocati di fare causa a Service per diffamazione: in una battaglia reputazionale sempre più shakespeariana, Figes è messo ai ferri corti dalle prove schiaccianti fornite dalla Polonsky. Allora, sempre di notte, cancella gli pseudonimi e accusa la moglie di essere lei l’autrice delle recensioni, perché non poteva accusare nessun altro, dato che l’indirizzo IP del computer corrispondeva a quello di casa sua! Come nel romanzo di Emannuel Carrère, L’avversario, in cui il protagonista preferisce sterminare l’intera famiglia che confessare di avere una falsa reputazione, Figes diffama la povera moglie avvocata, minaccia Service di lasciarlo in mutande per i soldi che dovrà pagare di causa, e infine crolla: confessa tutto, dicendo di non capire lui stesso il perché delle sue azioni, e accusando una grave depressione nervosa.

Una colossale guerra di reputazione in ambienti, come l’accademia e l’editoria e Internet, che si nutrono come vampiri di questo strano elisir del presente, che guida le nostre azioni contro qualsiasi razionalità. La reputazione - l’essere visto negli occhi degli altri – quel riflesso delle nostre azioni nello sguardo altrui, è forse la nostra passione più profonda. Forse, dietro all’Homo Oeconomicus razionale e interessato, esiste un’altra faccia delle nostre motivazioni, la Passione della Gloria, come la chiamava Hobbes, unica a garantirci di essere visti, di non svanire nel rumore collettivo. Ma attenzione: la reputazione consegna allo sguardo altrui il destino della nostra immagine, rendendolo manipolabile, fragile e fa così di noi stessi le prime vittime del nostro bisogno di esistenza sociale.

Reputation

Draft. Do not quote without permission. Submitted to the SAGE Encyclopedia of Philosophy of Social Science.

Reputation, from the verb puto in latin, meaning “counting, considering” plus the suffix re- that indicates the repetition, is the consideration of the value of an agent by other agents based on his or her past actions and creating expectations on the future conduct of that agent. Reputation is a special kind of social information: it is social information about the value of people, systems and processes that release information. Reputation is the informational trace of our past actions: it is the credibility that an agent or an item earns through repeated interactions. If interactions are repeated, reputation may conventionalize in “seals of approval” or disapproval or social stigmas.

The notion of reputation in social sciences has been mainly treated in economics. In Adam Smith’s liberal social theory, reputation is seen as a way of coordinating activities in a decentralized social space of transactions. According to Smith, in a free society, markets coordinate diffused knowledge in an asymmetrical way: people have a partial view of what other people know and how they will act. Also, given that most transactions occur over a lapse of time, parties have to trust each other that they will satisfy their reciprocal interest. These informational and temporal asymmetries call for efficient means of storing and retrieving information about possible partners in interactions. Reputation is more than pure information: it is evaluated information, that is, a shortcut of the many judgements and interpretations that people have cumulated about an actor. That is why people are interested in keeping a “good” reputation by signaling to potential business partners their trustworthiness.

In the rational choice tradition, reputation is modelled as a repeated game. These games raise the question on how you can signal your reputation before any interaction. That is, how you can signal your credibility in absence of information about your past behaviour. This question is studied within a rich body of work that goes under the name of Signaling Theory (Gambetta, 2009). Signaling Theory aims at solving a fundamental communication problem: Given an interaction in which interests diverge between the two parties, how can a party be certain of the qualities of the other party? Honest signallers will try to signal their good qualities (trustworthiness, accountability, strength), but dishonest signallers will try to do the same, by mimicking high-quality signals. Signaling theory may be traced back to the work of the American sociologist Thorstein Veblen. In his Theory of the Leisure Class, published in 1899, Veblen explains the display of wealth of the leisure class (luxury, expensive clothes, time-consuming unproductive activities such as sports) as a way of signaling its social position. Important developments of Signaling Theory go from the study of behavioural ecology (Krebs and Davies 1998) to the sociology and the economy of cultural tastes and lifestyles (Bourdieu, 1984). An agent emits signals in order to make a threat or a promise credible. Costly signals and robust signals, that is, signals that are difficult to fake, are those considered more credible (Zahavi, 1998).

The economist George A. Akelrof has shown that quality uncertainty is such a risky feature of markets, that reputation is needed: “Seals of reputation” in a markets are labels, certifications, guides, that is, all the devices that tend to reduce the informational asymmetry. A rational agent, according to Akelrof, has an interest in embodying these devices in order to compensate the cognitive deficit of the informational asymmetry.

Quality uncertainty and informational asymmetries have become crucial epistemological issues in contemporary information-dense societies. The vast amount of information available on Internet and on the media makes the problem of reliability and credibility of information a central issue in the management of knowledge. Informational items that do not come with some label, or seal of approval from the appropriate communities, are lost in the data deluge of the Information Age.

From the evaluator’s perspective, that is, the agent who has to filter information, reputation has an informational value. This has become a prominent issue in Web studies. Given that the structure of the Web is that of a reputational network, in which each link from a page to another can be read as a “vote” from a page to another, a number of algorithmic techniques have been developed to compute reputation of different entities on the Web: Recommender Systems, Collaborative Filtering and Reputation Systems (Resnick, 2000).

Collaborative forms of sharing ratings are also relevant in the study of Collective Intelligence (Landermore & Elster, 2012). People do not share information: they share evaluated and classified information that creates a “reputational stream” of shared judgements. The epistemological implications of the massive use of shared ratings in networked societies are huge: relying on other people’s judgements and authority challenges our epistemic responsibility. The reasons we trust collectively filtered ratings about an item or an agent are seldom explored. Choosing a doctor, an academic institution or a wine is a way of endorsing a tradition of values, a way of filtering information that is not always transparent and legitimate. Notorious biases in social networks - such as the Matthew effect, investigated by the sociologist of knowledge Robert Merton, according to which the nodes of a network that are more prominent have more probabilities to earn more reputation - create noise in the way reputation is diffused.

Other biases need further epistemological and cognitive inquiry. For example, people tend to form beliefs in order to acknowledge previously established reputations, such as voting for a certain party because a very well-reputed friend votes for that party. Also, reputations are resilient and may last over time even when the facts of the matters they are supposed to signal are no more there. For example, the prestige of institutions and corporations may last long time after their decay.

Reputation is a social commodity that needs to be handled in scientific way in order to avoid informational cascades, conformism and the perpetuation of received views.

Further readings:

Akelrof, G. (1970) "The Market for Lemons". Quarterly Journal of Economics, 84 (3), 488-500.

Bourdieu, P. (1984). Distinction. A Social Critique of the Judgement of Taste. Routledge and Kegan Paul.

Gambetta, D. (2009). "Signaling". In: P. Hedström, P. Bearman (eds) Oxford Handbook of Analytical Sociology, ch. 8.

Krebs, J. R., and Davies, N. B. (1998). An Introduction to Behavioural Ecology. Blackwell, Oxford.

Origgi, G. (2012)." Designing Wisdom Through the Web. Reputation and the Passion of Ranking". In H. Landermore, J. Elster (eds) Collective Wisdom. Cambridge University Press.

Veblen, T. (1899/2007). The Theory of the Leisure Class. Oxford World’s Classics.

Zahavi, A. (1998). The Handicap Principle. Oxford University Press.

Diario Brasiliano: 2. Reputaçao

A Porto Alegre, a cena da amici galleristi, discutiamo di vini e di quadri. Hanno una bella collezione di pittori del Rio Grande do Sul, niente da invidiare ai nostri De Pisis o ai Rosai, i Tirinnanzi, i Capogrossi, come se la storia del loro Novecento ripercorresse a distanza quella del nostro. Ma possibile che colori così diversi, esperienze politiche così lontane, abbiano creato due sequenze parallelle di autorità artistiche così simili? Ditemi sinceramente, sapreste distinguere il dipinto di Joao Fahrion (1898-1970) qui sotto da un nostro De Pisis?

O questo, quotatissimo, di cui ho dimenticato il nome, da un Rosai?


Eppure, per me sono quadri che non parlano, restano muti. Com'è possibile il paesaggio qui sopra, con la luce rosa da tramonto toscano, i siena bruciata dei tetti, la chiesetta, sia un paesaggio brasiliano? E perché riferirsi, deferire all'alrte del paesaggio italiano anche laggiù, nel Rio Grande do Sul? Quel che non mi torna è che sembra una tradizione importata, copiata, che non ha nulla di autoctono. Ma almeno di questi pittori riusciamo a parlare con i padroni di casa. I maestri sono evidenti, le tradizioni parallele, le influenze lampanti, come una storia parallela raccontata da due parti del mondo diverse.

E invece, mentre si procede d'epoca, i canoni iniziano a confondersi. Allora l'ultima misura è la reputazione delle espoziozioni internazionali (questo pittore è stato esposto alla Biennale di Venezia, quest'altro a NY) e il prezzo, ovviamente.


Cosa posso dire infatti del quadro qui sopra? A me ricorda Folon con un tocco di esotico nella bocca dentata del coccodrillo in basso a sinistra. Gli unici indizi di qualità su cui riusciamo a comunicare sono completamente indiretti. Il prezzo, le mostre, chi l'ha comperato. Il pittore non lo conosco, è qualcuno del Rio Grande do Sul, che ha un suo mercato locale, ma non è entrato nel Big Business internazionale. In qualche modo si vede, anche se forse le mie percezioni sono influenzate da quel che sapevo, che i miei amici mi hanno detto, permettendomi così di filtrare l'immagine in un certo modo. Però sì, si vede che parla a un pubblico locale.
Non si può dire lo stesso di artisti brasiliani, altrettanto sconosciuti per me, nei quali immediatamente si riconosce la traccia della "qualità globale": prendiamo Adriana Varejao, che, con un'operazione di pop-art mista a design e tradizione, dipinge giganteschi azulejos su tela, ed è quotata nelle aste di Christie's tra il 250 000 e il mezzo milione di dollari:



Si vede subito che i suoi interlocutori sono ben altri. Certi disegni evocano la grazia e la violenza di Kiki Smith, il formato gigante che riproduce il dettaglio a fondo, i grandi maestri pop come Roy Lichtenstein, l'azzurro e bianco dell'azulejos il più sofisticato design contemporaneo. L'arte contemporanea non è solo una questione di reputazione nell'occhio di chi guarda (chi è l'artista, chi lo compra, dove espone, quanto costa), ma anche di reputazione degli interlocutori della converazione immaginaria che ogni opera d'arte mette in atto. Ogni volta che creiamo un'opera d'arte abbiamo in mente un gruppo di pari, vivi o morti, grandi o piccoli, che avrebbero il loro posto nella sequenza di pensieri e gesti che hanno portato fino al nostro atto. La Varejao partecipa a una conversazione più raffinata, più colta e internazionale, rispetto al suo collega del quadro precedente. Lo si riconosce subito. Non è solo la sua qualità intrinseca, né solo gli effetti stregati e capovolti di reputazione, ma il modo in cui ha saputo orchestrare il prestigio di una conversazione.

Lo stesso per il vino. Ho approfittato della meravigliosa ospitalità dei miei amici per gustare una serie di bottiglie da 90/95 punti Parker, tutte argentine o cilene. Non che non esista vino brasiliano. Ce n'è anche troppo. Sauvignon, per esempio, proprio in Rio Grand Do Sul. E poi di tutto, Cabernet, Cabernet Franc, Merlot, Malbec. Eppoi mezzo mondo del vino sta accorrendo in Brasile a comperare terre da coltivare a vite. Ma il vino brasiliano non ha ancora trovato un suo cantore, forse l'eccessiva modestia di questo paese, non so. Benché identico, soprattutto nel Sud, a quello argentino, tranne per qualche produzione di punta, il vino brasiliano non ha per ora nessuna reputazione.

Assiaggiamo allora un Rutini 2009, la più antica produzione di vini argentini:


I padroni di casa mi stampano la critica del Wine Spectator, sempre per cercare di coordinare i nostri gusti e le nostre parole, cercare un linguaggio valutativo comune. Non basta un ranking per capirsi, ci vuole un ranking condiviso. Ora, i ranking condivisi, quando si viaggia lontano, sono quelli più visibili, i ranking dominanti, non certo quelli di maggiore qualità. E infatti arrivano a cena due ricchi commercianti brasiliani, gente che viaggia il mondo e che ci tiene al "consumo ostentatorio" di beni, per segnalare una certa reputazione da "signori". Parliamo di champagne. Anche se non sono un'esperta, la mia coversazione è troppo specialistica per loro. Non conoscono la differenza tra un Blancs de Blancs, ossia uno champagne fatto solo da uve chardonnay, e una cuvée prestige. Non sanno che le uve dello champagne sono il Pinot Noir, lo Chardonnay et il Pinot Meunier, non sanno cosa significa Millésimé.
Tutto ciò che conoscono sono i nomi di Moët & Chandon et Veuve Cliquot, ossia i "picchi" di celebrità nel mercato dello champagne, i nomi più reputati internazionalmente. Ma nessuno a Parigi direbbe che i migliori champagne sono questi. Questione di conversazione. Ogni circolo ha le proprie autorità. E' per questo che è così difficile "farsi bello" lontano dai propri interlocutori abituali. Perché non ti capiscono o ti prendono per uno che non sa le regole del gioco.

Il gioco della reputazione è un gioco linguistico complesso, che bisogna saper padroneggiare. I segnali di reputazione sono in gran parte ancorati a contesti locali. Solo pochi indici sorvolano il mondo intero. Bisogna coordinare il valore delle proprie autorità alle autorità degli altri, smussare i valori, trovare delle griglie comuni di notazioni. E' per questo che il successo di Robert Parker nel mercato dei vini fu così imponente. I Parker Points sono una griglia estensiva, da 50 a 100, molto più informativa quindi dell'uno, due o tre bicchieri delle guide Michelin o Veronelli. Inoltre i punti parlano a tutti, molto di più delle complicate descrizioni dei critici. Se avessimo lo stesso sistema di notazione, diverso dal prezzo, per l'arte contemporanea (Questo è stato giudicato tre pennelli, questo quattro) sarebbe forse più facile parlarne senza fingere expertises che non esistono.

Séminaire d'épistémologie sociale 2011-2012 EHESS: La Réputation

Gloria Origgi

Séminaire EHESS - EPISTEMOLOGIE SOCIALE 2011-2012

Du 7 novembre 2011 au 13 février 2012

Tous les lundis de 15h à 17 h, 105 Bd. Raspail 75006, salle 11.

La Réputation

Toute interaction sociale comporte une dimension d’évaluation, de jugement réciproque, une perception de qui nous sommes que notre conduite donne à l’autre, la constitution d’une image de nous qui se construit dans les yeux des autres. Toute interaction sociale comporte ainsi un contrôle de la part du sujet de cette image qui se construit dans l’esprit de l’autre, une adaptation à la réaction des autres. Cette dimension évaluative de l’interaction sociale, cette génération d’opinions de l’un sur l’autre, est la réputation.

Dans ce séminaire, nous explorons la dimension épistémologique, sociale et morale de la réputation : comment utilisons-nous les réputations des autres et leurs évaluations pour extraire de l’information à leur sujet ? Est-ce que ces pratiques épistémiques de s’appuyer sur la réputation pour nos jugements, sont toujours légitimes? Comment se construit la réputation des personnes et des objets, quel est son rôle dans l’espace des interactions sociales et marchandes ? Comment les index et les rankings partagés par différentes institutions façonnent un monde de notations économiques, politiques, sociales et épistémiques? L’extraction de l’information à travers d’indices indirects de réputation est devenue l’une des questions les plus urgentes des sociétés à forte densité informationnelle. Quelles sont les pratiques cognitives, les normes sociales, la déférence aux autorités qui guident nos usages d’indices réputationnels dans le choix d’un produit, d’une information sur Internet, d’un médecin, ou d’un investissement économique?

Le séminaire aborde la question avec une méthode interdisciplinaire, en s’appuyant sur l’expertise des différents intervenants dans les domaines de la philosophie, de la sociologie, des sciences cognitives, de l’économie, de la morale et des relations internationales

Calendrier

Lundi 7 novembre 2011 :

Gloria Origgi : Eléments d’une théorie du signal. De Veblen à Bourdieu.

Textes :

Thorstein Veblen: Théorie de la classe des loisirs, Gallimard, Paris.

Diego Gambetta: “Signalling”, in P. Hedström, P. Bearman (eds) Oxford Handbook of Analytical Sociology, 2009.

Lundi 14 novembre 2011 :

Gloria Origgi : Dans le regard des autres. L’usage épistémique de la réputation.

Textes:

G. Origgi: “Un certain regard. Pour une épistémologie de la réputation”

G. Origgi: “Wine Epistemology. Reputation and Rating Systems in the World of Wine” in. B. Smith (ed) Questions of Taste, Oxford University Press.

Lundi 21 novembre 2011 :

Gloria Origgi : La construction de la réputation académique. Bons et mauvais usages des dispositifs d’évaluation de la recherche.

Textes:

M. Rossner, H. Van Hepps, E. Hill (2007) “Show me the Data”, in Journal of Cell Biology, 2007, vol. 179, n. 6

G. Origgi, J. Simon (2011) Scientific Publications 2.0., Special issue of Social Epistemology, 24 (3).

M. Lamont (2010) How Professors Think, Chicago University Press.

Lundi 28 novembre 2011 :

Dan Sperber (Institut Nicod et Université de Budapest): Moralité et réputation dans une perspective évolutionniste

Textes:

D. Sperber, N. Baumard: “Morality and Reputation: An Evolutionary and Cognitive Perspective”, Mind & Language, à paraître.

Lundi 5 décembre 2011 :

Gloria Origgi : A qui fait-on confiance ? La qualité des indices réputationnels dans l’extraction de l’information.

Textes :

D. Sperber, O. Mascaro, G. Origgi et al. (2010) “Epistemic Vigilance”, Mind & Language, vol. 25.

G. Origgi (à paraître 2012) “Epistemic injustice and Epistemic Trust” Social Epistemology.

Lundi 12 décembre 2011

Ariel Colonomos (CNRS – CERI – SciencesPo) : La réputation des Etats : le rôle des agences de notation dans la politique et l’économie internationales.

Textes :

Colonomos (2010) « Sovereign Ratings as Normative Predictions » in Governing the Future, http://www.interdisciplines.org/Governing-the-Future

Lundi 9 janvier 2012

Gloria Origgi : La réputation comme classification. Les taxonomies du Web.

Textes :

G. Origgi (à paraître) Designing Wisdom Through The Web : Reputation and the Passion of Ranking, in H. Landermore, J. Elster (eds) Collective Wisdom, Cambridge University Press.

J. Simon, G. Origgi (2010) « Is Reputation a Form of Classification ? » Proceedings of the ISKO Conference, Rome, 13 février 2010.

Lundi 16 janvier 2012

Jon Elster (Collège de France et Columbia University, NY) : Reputation and Character

Textes :

J. Elster (2007) « Reputation and Character » présenté au Workshop sur la Réputation, Fondazione Olivetti, Rome, avril 2007.

Lundi 23 janvier 2012

Gianluca Manzo (CNRS – GEMASS – Sorbonne) : Syrènes et raisins dans l’émergence des hiérarchies de status.

Textes :

R. Gould (2002) « The Origins of Status Hierarchies. A Formal Theory and an Empirical Test » American Journal of Sociology, 107 (5).

Lundi 30 janvier 2012

Barbara Carnevali (IEA – Paris) Philosophie du prestige.

Textes :

B. Carnevali (2010) « Snobbery. A Passion of Nobility », in L. Ballerini et al. (eds) Navigatio Vitae, New York, Agincourt Press.

B. Carnevali (2008) « Proust. Philosophie du prestige » in M. Carbone, B. Sparvoli Proust et la philosophie aujourd’hui, ETS, Pise.

Lundi 6 février 2012

Pierre-Michel Menger (EHESS-CESPRA) : Talent et réputation

Textes :

P.M. Menger Portrait de l’artiste en travailleur, Seuil, La République des idées.

Lundi 13 février 2012

Gloria Origgi : Une mauvaise réputation : relations de confiance déviantes et réseaux réputationnels anti-méritocratiques.

Textes :

D. Gambetta, G. Origgi (2011) « The LL-Game, or the curious préférence for low quality outcomes », Politics, Economics and Philosophy, à paraître.

D. Gambetta (2009) Codes of the Underworld, Princeton University Press

Who Gets to Keep Secrets?

Here is my reply to Daniel Hillis' question on EDGE. Do not quote without permission.

Secrecy is the forbidden fruit: you want to know more even at the risk of loosing the heavenly security of the Garden of Eden. Speech is power: some information is so potent that it could be dangerous. God created the universe with speech and he put the forbidden tree to remind to his creatures that they could not get the overall picture, that some files remained classified.

In classical mythology, those who steal secrets from God are damned heroes, like Prometheus, who stole the secret of fire from Zeus. Being human is a damned heroic destiny: we are scavengers, scraping off layers of lies and prohibitions to reach bitter truths.

Truth is not just an epistemic commodity: it is a human value. It mixes the needs of sincerity, accuracy and honesty that are essential to trust each other, to feel that we belong to the same species, that we are playing the same game.

But secrecy is not a sacred value: it is perceived as an abuse of power. It may have rational motivations, it may be indispensable in order to keep order and peace, but the secret-keeper never has the part of the hero, apart from extreme cases when lying is a way of saving people against an oppressive power that wants to brutally extort information to act in an evil way.

State secrecy is not a clear principle: no constitutions in the Western world endorse State secrecy as a legal or moral principle. It is an old privilege of sovereigns that has taken different shapes in the political history. It goes from the British Majesties' privilege of the Habeas Corpus, which overrules local authorities, to the Macchiavellian precepts to the Prince, who must classify some information in order to succeed in governing the people. What is called Raison d'Etat, is the privilege of the sovereign to act "out of law" for the State's interests. That is why it is so difficult these days to see State secrecy as legitimate, and to see those who violate it as traitors.

In our times, the first time United States advocated exclusion of evidence in a trial based only on affidavit was in 1953, in the United States vs. Reynolds case which involved the crash of a military plane whose mission had to be kept secret.

That is to say: it is difficult to have a spontaneous sympathy for the secrets' holders, and the damned heroes à la Julian Assange have all their chance to gain popular consensus.

Also, we come out from a decade in which truth-wars have been at the centre of the most difficult political choices, such as the Iraq invasion. For those who have studied the whole story, the balance between secrecy and security was really odd: the report from the British Intelligence on which Colin Powell based his speech at the UN, contained a major plagiarism from the journal Middle Eastern Studies. The following British report had been "sexed up" in order to affirm that an Iraq nuclear attack was possible in 45 minutes.

But what are the truths we value in the information society? Now that the Information Age is leaving its place to the Reputation Age, we want certified truths, attested by authoritative sources: we want the seal of quality that warrants us on where the truth come from, who is the authority endorsing it. Plain, factive truths, like plain facts, don't exist anymore: we trust a chain of production of truths, with its labels and legitimacies. The naked "truth" that leaks from unknown sources is unreadable, it is a noisy voice that we do not know what to do with. Yet, the Wikileaks scandal comes from the fact that many newspapers have given credit to the source, thus showing that they endorse this chain of production. They have provided the reputation these naked truths needed.

We have to understand better how these chains of reputation of information are constructed and endorsed. We have to take the epistemic responsibility of asking ourselves why we trust news or an information provider. And perhaps, with the power of collaborative work on the Web, we can contribute in giving the appropriate labels to the information we are able to control, thus contributing to the damned human enterprise of unveiling the forbidden truths.

On the Epistemic Value of Reputation. The place of ratings and reputational tools in knowledge organization

Submission for the Eleventh International ISKO Conference 2010
Paradigms and conceptual systems in KO
February 23, 2010 – February 26, 2010

by Gloria Origgi and Judith Simon


Abstract: In this paper we want to explore the epistemological relevance and value of reputation understood as evaluative social information. Using reputation to classify and assess an agent or an item can be epistemologically useful in the absence or - as is especially relevant today - overabundance of information. However, in order to be and remain epistemically useful and ethically just it has to be open to constant scrutiny and revision. We will introduce a model of rational consensus as an example for the rational use of reputation for epistemic purpose before analyzing different reputational tools on the web. We will conclude our paper with a critical comment on the potential danger of using social information to evaluate information and knowledge claims, resp. to warn from epistemic injustices on the web and elsewhere.

1: Introduction

What is that scarlet piece of tissue in the shape of an A sewn on Hester Prynne's gown in Nathaniel Hawthorne's masterpiece The Scarlet Letter? Is it a symbol of her sin, a "badge of shame", an indelible sign of her community's contempt? Is it a cruel reminder of her past, a succinct history of her misdeeds? Imagine that in the same colonial New England village, you do not have just a badge for the poor Hester, but each member of the community wears a letter that represents some past records of its owner. We can also imagine sets of identical badges worn by members of the community who have similar records: sinners, heroes, drunkards....Imagine that the elders of the community have the right to attach these labels to the villagers. Their judgments, based on their purported wisdom, become an easy way for the villagers to dispose of a basic classification of social types within the community that will allow them to manage their relations with others, to make inferences and predictions about their behavior, that is, construct a basic "social map" that will help them orient in their society. Morally this may be questionable, but epistemologically it can be useful.
We want to explore in this paper, the epistemic value of this type of social information, that is, reputation, while being aware of the ethical and political problems that might come with using it for epistemic purpose. Using the judgment on past records to classify an agent or an item can be epistemologically useful in the absence or - as is especially relevant today - overabundance of information. But it has to be and remain open to constant scrutiny and revision to be epistemically useful and ethically just.

2: Reputation as Evaluative Social Information

Reputation is a special kind of social information: it is social information about the value of people, systems and processes that release information. We want to explore here the relationship between this special form of social information - that implies an evaluative stance - and the processes of knowledge organization and evaluation. More precisely, we want to argue not only that (1) we make use of other people's reputations to evaluate information, but also (2) within systems, like the Web, that make possible the easy and dynamic organization and re-organization of knowledge, our own rankings may determine new content and generate new categories.

Reputation is the informational track of our past actions, it is the credibility that an agent or an item earn through repeated interactions. We would like to defend an epistemological perspective according to which relying on reputational cues is an efficient way of shaping the too rich informational landscape around us by creating new relevant categories. Experts and authorities not only bloom where information is scanty, but also, and most crucially, in an information-dense world in which filtering out relevant information is our prominent cognitive activity. The epistemological enquiry we are advocating here implies that reputation and rating systems are an essential ingredient of collective processes of knowledge and play a cognitive role in extracting information. In an information-dense environment, where sources are in constant competition to get attention and the option of the direct verification of the information is often simply not available at reasonable costs, evaluation and rankings are epistemic tools and cognitive practices that provide an inevitable shortcut to information. We assume that there is no ideal knowledge that we can adjudicate without the access to previous evaluations and adjudications of others. No Robinson Crusoe’s minds that investigate and manipulate the world in a perfect solitude. Our modest epistemological prediction is that the higher is the uncertainty on the content of information, the stronger is the weight of the opinions of others in order to establish the quality of this content.
Of course, this opens the epistemological question of the epistemic value of these rankings and reputation mechanism, that it, to what extent their production and use by a community changes the ratio between truths and falsities produced by that community and, individually, how an awareness of rankings should affect a person’s beliefs. After all, rankings introduce a bias in judgment and the epistemic superiority of a biased judgment is in need of justification.

3: Rational Model for the Epistemic Use of Social Information

To illustrate how reputation understood as social information that comes with an evaluative stance can rationally be used for epistemic purpose, we introduce a formal model of rational consensus. In “Rational Consensus in Science and Society” Keith Lehrer and Carl Wagner develop their formal theory of consensus that rests upon the employment of consensual probabilities, utilities and weights and is meant to provide a model for rational decision making processes in science and society more generally ((Lehrer and Wagner 1981))). To our mind, this model is actually a model of how to quantify and use reputation for epistemic purpose.
Lehrer & Wagner argue that for decision making processes to be rational, it is central that all relevant information for the topic of concern has to be used ((Lehrer and Wagner 1981)). However, this spectrum of available information - for instance concerning disputes on scientific theories - is not limited to experimental information, but should also include the opinions of experts on other experts in the field. Lehrer calls this second type of information social information ((Lehrer 1990)) – and we call it reputation, i.e. social information that comes with an evaluative stance.
To illustrate how this social information might be used for epistemic purposes, Lehrer uses the so-called “expert dilemma” as a scenario. The expert dilemma describes the frequently encountered situation in which a decision has to be made despite the fact that evidence for answering a question is inconsistent and different experts recommend different options. An example would be whether or not to release a new medication or vaccine before all clinical trials are completed when facing the threat of an epidemic. The basic question of Lehrer & Wagner ((Lehrer and Wagner 1981)) is the following: If scientific dissent is prevailing, but suspension of judgment is not an option, how should the conflicting information be used to reach a consensual conclusion? “Consent on the reputation of the experts in order to decide on the issue” could be the motto of their approach. Social information is used here as a crucial factor to decide on content information.
Using reputation as a decisive factor for factual matter rests upon the assumption that each expert in a certain community might be more or less reliable or competent with respect to the specific question at stake. If that is the case, it would most rational to include each expert’s answer weighted by his competence regarding the issue. And the best way to assess the competence of each expert would be to use the aggregated reputation judgment of all other experts because they are most likely in the best position to judge the competence of their peers.
Lehrer & Wagner develop a quite complex mathematical model that describes an iterative and collective process to reach quantitative values for the reputation of each scientist ((Lehrer and Wagner 1981)). The basic idea however, is quite simple. The first step in this model consists in each expert giving a weight to all other experts summarizing all his information about the other’s expertise and reliability concerning the issue at stake, in other words: he gives a quantitative indicator of what he considers to be the reputation of the scientist with respect to topic at hand. In a second step, the average reputation values for each scientist are calculated with a specific algorithm and then laid open. Then in the second round, each expert has to reassess the reputation value he has given to all other members of the community, i.e. she has the chance to revise his or her judgment taking into account the average weights which the other members of the community have given to their fellows. Similarly to Delphi-studies in the social sciences, this process is then ideally repeated until finally a consensual weight for each member of a community is achieved ((Linestone and Turoff 2002)).
The idea is that, if you are less secure about the reputation of a certain researcher, you might tend more towards the group average in your second vote. If you are very sure about the reputation of someone, however, you will not let yourself be influenced by this average. If everyone acts this way, that is considered to be most rational, then the consensus that is finally achieved is considered to be the most rational consensus. Crucially, once these consensual weights are achieved, they can be applied to answering the question of concern by weighting each member’s vote on the issue with their consensual personal weight of reputation.
So, what should be obvious is that reputational cues, i.e. social information about other people that is evaluative, are being used – and that they are useful. Clearly, not all epistemic usage of reputation cues has to follow such a formal method. Quite on the contrary, ratings and other reputational tools might be used in a variety of different ways on the Web and our everyday life more generally. Nonetheless, Lehrer & Wagner’s model delivers a clear example of the potential that reputation understood as social information from an evaluative stance, can have for epistemic tasks ((Lehrer and Wagner 1981)).

4: Reputational Tools on the Web

What the Web makes possible today is an algorithmic treatment of methods of gathering social information to extract knowledge. Ratings and rankings on the Web are the result of collective human registered activities with artificial devices. However, the control of the heuristics and techniques that underlie this dynamics of information may be out of sight or incomprehensible for the users who find themselves in the very vulnerable position of relying on external sources of information through a dynamic, machine-based channel of communication whose heuristics and biases are not under their control. Thus, the reputational tools that are proliferating on the Web should be scrutinized by epistemically responsible users who do not want to accept too naïvely the outcome of a process they do not control.
The role of these reputational tools to filter information is getting more and more central in our Web-based epistemic practices ((Origgi 2009), (Origgi 2007)). And even more explicitly, we state that those systems that embody an access to others’ judgments and rankings are rapidly outperforming, in terms of reliability, the random aggregation of multiple judgments and preferences on which many systems were based, as it is shown by the growing impact of the Web 2.0 on our epistemic practices. A growing number of examples of architectures on the Web show how these rankings work to produce new arrangements of information.
The Web 2.0 has provided the underlying networking structure to share ranked preferences. If you take the Web of the early years of 2000, one of the main feature that attracted much attention and criticism was the possibility to "customize" information for each user in order to fit each one's special needs and purposes of navigation. The endless potential of re-organization of the new, dynamic, information architectures based on the aggregation of chunks of contents according to specific rules (in contrast with the rigid tree-structures of the first-generation of web pages) opened the opportunity to create and organize "content on demand". News websites, online stores, search-engines, etc thus started to provide "My-" features to the users, that is, easily arranged customized pages with targeted news and other information for the users, personalized lists of products, personalized recommendations etc. This gave rise to a series of positive expectations and negative warnings, such as the risk of neglecting other people's points of views and perspectives by concentrating only on personally relevant information (cf. (Sunstein 2002)). Now, thanks to the social Web, these systems are evolving into systems of shared preferences, in which people can rely on someone else's preferences and ranking to construct their own categorization of information. Examples of this preferences-sharing are website such as Del.icio.us in which you can share your bookmarks with other people, or Flickr, in which, for each uploaded photo, not only you can see who uploaded it, but also who are the profiles that added it among their favorite pictures. Combining information about who comments on an image, who adds it as favorite, who tags it and how, Flickr now provides a new feature for browsing images: interestingness, http://www.flickr.com/explore/interesting/ which is an example of preference-based tools of categorization. As a Flickr user, I can decide to generate new categories of contents on the basis of an interestingness scale. A new category of the most interesting images on Flickr today is thus generated by sorting others' preferences. The success of this "fluid" way of constructing concepts and categories may depend also on the fact that it matches our cognitive capacities: it has been shown by cognitive psychologists (cf. (Barsalou 1995)) that concepts and mental categories are flexibly constructed in context.
In this perspective, the EC project LiquidPublication, (http://project.liquidpub.org/) in which both authors are involved, aims at developing "liquid" architectures for producing, accessing and gathering scholarly information on the Web. Take for example the very concept of an academic journal: it is a selection of content based on a series of criteria of categorization: ISBN number, date of issue, etc. What we are working at in this project is a model of "Liquid Journal" which easily allows people to create selections of papers, articles, blog entries as a "My-journal" and then share them on the Web. One can imagine that, with the diffusion of such a model, the very category of "academic journal", or "journal issue" will be re-created by this particular form of information sharing, in which a user X can "conceptualize" a journal issue as for example: "all the content that the user Y is selecting in her journal". Here again, preferences of a user can be used by other people to re-organize information in a creative way. Virological examples of information diffusion based on a Twitter-logic of followers and leaders may be another example worth mentioning of reputational tools that create new categories of information.
Although the information-dense environment provided by the Web is the obvious locus in which examples bloom, we do not think that our analysis should be restricted to the case of the Web: in many other domains where information about the items at stake is very costly or difficult to obtain, reputational cues become an unavoidable way of organizing knowledge. Different cultural domains such as wine labeling systems and academic citation systems are based on rating devices that classify the underlying information by evaluating it (see (Origgi 2007), (Origgi 2009)).
5: Epistemic Injustices: On the Dangers of Using Social Information for Epistemic Purposes
The model of rational consensus as well as the Web applications that we have introduced are clearly examples of how reputation can be used to decide on content information, resp. on how social and content information might be productively merged to achieve better epistemic results. However, where there is use, there also is potential misuse. And in the case of reputational cues, these dangers might be inherent in the very concept of reputation as the “recognition by other people of some characteristic or ability” ((Merriam-Webster-Online-Dictionary 2009)).
More precisely there are two threats. First of all, the use of reputation to assess content can be epistemically beneficial while being morally questionable. This problem already becomes obvious in the first example we chose to open this article: Hawthorne’s A-shaped scarlet piece of tissue. Although classifying someone as a sinner, hero or drunkard – or as an expert, layperson or lobbyist - based on some cues might prove epistemically useful in certain situations, we would have to decide whether we are willing to pay the moral price of possible discrimination that comes with such stereotypical evaluation. More generally, once social information is taken into account to rate the quality of content, the door is open for social biases, prejudices and discrimination, which are as prevalent on the Web as in the societies that have developed and maintained it. These problems are not new and have long been identified for science and other epistemic fields by feminist epistemologists. In addition to raising awareness about these problems, various scholars have also developed tools and strategies to counter these epistemic injustices ((Fricker 2007), (Scheman 2001), (Alcoff 2001)). Miranda Fricker for instance distinguishes between testimonial and hermeneutic injustices as two instances in which someone is wronged in his capacity as a knower based on his social position. According to her “testimonial injustice occurs when prejudice causes a hearer to give a deflated level of credibility to a speaker’s word, whereas hermeneutic injustice “[...] occurs at a prior stage, when a gap in collective interpretative resources puts someone at an unfair disadvantage when it comes to making sense of their social experience” ((Fricker 2007) 1). Clearly, both forms of injustice are easily conceivable when reputational cues and their epistemic usage are not critically reflected upon and kept open for constant scrutiny and revision.
The second problem concerns the limits of the epistemic usefulness of this type of information itself. The first question is how you calculate the reputation of someone else in the first place, resp. which proxies you use. Do you use the person’s academic development, his institutional background, some form of communal evaluation, such as ratings or recommendations that he has received from other people as a cue to assess someone’s reputation? Do you rely on your own experience with her? On some indicator of the quality of his former research? On her track record of different academic achievements? Her H-index or impact factor? Which of these proxies are valid and which are not? The second crucial questions concerns the stability of reputation, resp. the way you deal with evidence that supports or contradicts your view on the reputation of others. When, under which conditions and up to which point of counter-evidence or you warranted in keeping your reputation value for someone or something? Clearly, these issues as crucial as they are cannot be answered given the brevity of this paper. However, if we want to explore the utility of reputation for epistemic purposes, we have to analyze the potentials and possible dangers very carefully. That reputation is used to assess information and epistemic claims goes without saying – and it comes with benefits as much as with problems. So the question should be less how to avoid using reputation as epistemic tools, but rather how to use them wisely.
6: Conclusion
Our preliminary analyses indicate that ratings and reputational tools in knowledge organization have epistemological, cognitive, practical as well as ethical implications. From an epistemological point of view, a priority of rating tools and reputational scales over classification leads to a re-conceptualization of the “facts/values” dichotomy. Another epistemologically pressing question concerns the validity of reputation mechanism as epistemological tools. How epistemically warranted is the use of these tools? Is it just based on blind and imperfect heuristics that have a serendipitous effect on our search of information, or is it possible to conceive second order epistemic criteria that allow us to pry apart “good” and “bad” practices of trust and reliance on these reputational metrics?
For cognition, this implies to take into account a pragmatically oriented way of creating concepts and categories (i.e. the most “valued”, items preferred by “x”), as it has already been argued in some works in cognitive psychology (cf. (Barsalou 1995)). From a practical point of view, this perspective may help to rethink the bottom up/top down distinction in designing categories by suggesting ways in which rating systems can serve as middle-ground categorizations that are neither imposed from above, not completely generated from spontaneous tagging. Rather they are user-driven meta-categorizations that inform the users.
The ethical and political aspects become obvious when taking feminist critique concerning the danger of epistemic injustices into account. Miranda Fricker’s emphasis of the danger of testimonial and hermeneutic injustices are particularly pressing when reputational cues are used uncritically. It is especially when reputation mechanisms become automatized in algorithms, there is a clear danger that epistemic injustices are inscribed in and reinforced by technology. Such an entanglement between ethics and epistemology in information design has been shown for trust-aware recommender systems((Simon 2008; Simon 2009)). Different trust metrics not only yield to different search results, but that they also correspond to different views concerning the organization as well as even more fundamentally the very concept of knowledge. Moreover, different trust metrics value different people differently and depending on the algorithm, some users are automatically silenced and “sorted out” ((Bowker and Star 1999)), while others “count”. Thus, when developing reputational tools, the possibility of injustices has to be accounted for.
This example suggest that a purely epistemological or cognitive analysis of using reputation for epistemic purposes will not suffice for knowledge organization: the goals and standards for knowledge organization and epistemic practices have to be discussed and decided upon taking political and ethical considerations into account. Reputational tools open up new possibilities for knowledge organization, but they also bring with them their own problems. Raising awareness for the values as well as the dangers of using reputational cues for epistemic assessment will be the major goal of our talk.

References

Alcoff, L. M. (2001). On Judging Epistemic Credibility: Is Social Identity Relevant? Engendering Rationalities. N. Tuana and S. Morgen. Albany, SUNY Press: 53-80.
Barsalou, L. (1995). Flexibility, structure and linguistic vagary in concepts. Theories of Memory. A. F. Collins, S. E. Gathercole, M. A. Conway and P. E. Morris, Psychology Press, Taylor and Francis.
Bowker, G. C. and S. L. Star (1999). Sorting Things Out: Classification and Its Consequences. Cambridge, MIT Press.
Fricker, M. (2007). Epistemic Injustice. Power and the Ethics of Knowing. Oxford, Oxford University Press.
Lehrer, K. (1990). Metamind. Oxford, Claredon Press.
Lehrer, K. and C. Wagner (1981). Rational Consensus in Science and Society. Dordrecht, Reidel.
Linestone, H. A. and M. Turoff (2002). The Delphi Method: Techniques and Applications, Addison-Wesley Publishing Company.
Merriam-Webster-Online-Dictionary (2009). Reputation. Merriam-Webster Online Dictionary.
Origgi, G. (2007). Wine epistemology: The role of reputational and rating systems in the world of wine. Questions of Taste. B. Smith. Oxford, Oxford University Press: 183-197.
Origgi, G. (2009). Designing wisdom through the web. The passion of ranking. Collective Wisdom. J. Elster and H. Landermore. Cambridge, Cambridge University Press.
Scheman, N. (2001). Epistemology Resuscitated: Objectivity as Trustworthiness. Engendering Rationalities. N. Tuana and S. Morgen. Albany, SUNY Press: 23-52.
Simon, J. (2008). Knowledge and Trust in Epistemology and Social Software/ Knowledge Technologies. Culture and identity in knowledge organization: Proceedings of the Tenth International ISKO Conference. C. Arsenault and J. T. Tennis. Montréal, Canada, Würzburg: Ergon: 216-221.
Simon, J. (2009). MyChoice & Traffic Lights of Trustworthiness: Where Epistemology Meets Ethics in Developing Tools for Empowerment and Reflexivity. Proceedings of the 8th International Conference of Computer Ethics, Corfu, Nomiki Bibliothiki.

The Age of Reputation

This is my 2009 answer to the annual EDGE question. This year's question was WHAT WILL CHANGE EVERYTHING? Easy, don't you think? Do not quote without permission.

THE AGE OF REPUTATION

When asked about what will change our future, the most straightforward reply that comes to mind is, of course, the Internet. But how the Internet will change things that it has not already changed, what is the next revolution ahead on the net, this is a harder matter. The Internet is a complex geography of information technology, networking, multimedia content and telecommunication. This powerful alliance of different technologies has provided not only a brand new way of producing, storing and retrieving information, but a giant network of ranking and rating systems in which information is valued as long as it has been already filtered by other people.

My prediction for the Big Change is that the Information Age is being replaced by a Reputation Age in which the reputation of an item — that is how others value and rate the item — will be the only way we have to extract information about it. This passion of ranking is a central feature of our contemporary practices of filtering information, in and out of the net (take as two different examples of it — one inside and the other outside the net - www.ebay.com and the recent financial crisis).

The next revolution will be a consequence of the impact of reputation on our practices of information gathering. Notice that this won’t mean a world of collective ignorance in which everyone has no other chances to know something than to rely on the judgment of someone else, in a sort of infinite chain of blind trust where nobody seems to know anything for sure anymore: The age of reputation will be a new age of knowledge gathering guided by new rules and principles. This is possible now thanks to the tremendous potential of the social web in aggregating individual preferences and choices to produce intelligent outcomes. Let me explain how more precisely.

One of the main revolution of Internet technologies has been the introduction by Google of the « PageRank » algorithm for retrieving information, that is, an algorithm that bases its search for relevant information on the structure of the links on the Web. Algorithms such as these extract the cultural information contained in each preference users express by putting a link from a page to another with a mathematical cocktail of formulas that gives a special weight to each of these connections. This determines which pages are going to be in the first positions of a search result.

Fears about these tools are obviously many, because our control on the design of the algorithms, on the way the weights are assigned to determine the rank is very poor, nearly inexistent. But let us imagine a new generation of search engines whose ranking procedures are simply generated by the aggregation of individual preferences expressed on these pages: no big calculations, no secret weights: the results of a query are organized just according to the « grades » each of these pages has received by the users that have crossed that page at least once and taken the time to rank it.

A social search engine based on the power of the « soft » social computing, will be able to take advantage of the reputation each site and page has cumulated simply by the votes users have expressed on it. The new algorithms for extracting information will exploit the power of the judgments of the many to produce their result. This softer Web, more controlled by human experiences than complex formulas, will change our interaction with the net, as well as our fears and hopes about it. The potential of social filtering of information is that of a new way of extracting information by relying on the previous judgments of others.

Hegel thought that universal history was made by universal judgments: our history will be written from now on in the language of « good » and « bad », that is, in terms of the judgments people express on things and events around them, that will become the more and more crucial for each of us to extract information about these events. According to Frederick Hayek, Civilization rests on the fact that we all benefit from knowledge we do not possess: that’s exactly the kind of civilized cyber-world that will be made possible by social tools of aggregating judgments on the Web.

Du vin et du web

Soirée Science 2.0
Originally uploaded by Enro

Entretien avec Marc Foglia publié dans rue89

Interview avec Gloria Origgi, chercheuse au CNRS, s’intéresse au phénomène de la croyance, à la construction des valeurs, et plus spécifiquement à la manière dont le web modifie et accélère la construction de la connaissance collective.

Je voudrais revenir sur le bel article [1] que vous avez publié dans La Vie des Idées, sur « la passion d’évaluer ». Avant de travailler sur le fonctionnement de la réputation sur Internet et ses effets, vous aviez étudié la réputation dans le monde du vin?

Lorsqu’on entre en contact avec un domaine de connaissance nouveau, ce sont les opinions des autres, leurs valeurs et préférences qui déterminent notre accès aux faits.

Le vin était donc pour moi un prétexte intéressant pour développer mes travaux en épistémologie. J’ai observé des novices adultes entrer en contact avec un corpus culturel nouveau, auquel ils doivent apprendre à donner de la valeur.

Quand ils souhaitent acheter une bouteille, les gens doivent d’abord s’orienter, se créer un paysage dans lequel ils pourront se retrouver. Les systèmes de classification du vin changent, évoluent -surtout sur les marchés nouveaux- mais il ne s’agit pas d’une simplification.

Il s’agit plutôt d’une différenciation de plus en plus fine, qui permet de stocker sur une étiquette une grande quantité d’indices réputationnels sur la qualité du vin.

Dans notre société à forte densité informationnelle, que ce soit sur Internet, ou dans un hypermarché avec des centaines de bouteilles de vin sur les rayons, le filtrage de l’information et les échelles de valeur prennent une importance essentielle. Comment s’acquiert cette information sur l’information? L’opinion des autres opère un filtrage.

Dans votre article, vous décrivez un nouvel âge de l’Internet : l’âge du filtrage de l’information aurait succédé à l’âge du stockage. Comment peut-on décrire la courte vie de l’Internet, quelles étapes se dégagent aujourd’hui? Comment caractérisez-vous l’âge actuel, est-ce l’âge de la maîtrise, après celui de l’enthousiasme?

Je suis convaincue que nous nous trouvons face à un changement de paradigme fondamental dans notre rapport à la connaissance: de l’âge de l’information, nous sommes en train de passer à l’âge de la réputation, dans laquelle l’information n’aura de la valeur que si déjà filtrée, évaluée et notée par les autres.

Il s’agit d’une transformation radicale, due en partie aux nouveaux moyens techniques de diffusion de l’information, et surtout à l’usage social de ces moyens.

Depuis la création du PageRank, en 1998, il n’y a pas eu d’innovation technique décisive dans l’Internet. Les innovations sont venues plutôt du versant des applications sociales sur le web.

Je vois un contraste énorme entre la créativité d’avant 2000, et la normalisation après l’éclatement de la bulle et le changement de gouvernement aux Etats-Unis.

L’administration Clinton (en particulier Al Gore) a intensément soutenu le développement de l’Internet: à la fin des années 90, les Etats-Unis disposaient d’un potentiel unique au monde, et dont ils entendent bien faire profiter le monde entier. Après, avec l’administration Bush, l’âge de glace commence.

Dans les années 90, ce sont des politiques institutionnelles qui ont rendu possible la créativité sur l’Internet: ainsi, la norme « end-to-end », selon laquelle l’intelligence est concentrée aux extrémités du réseau, dans les différentes applications créées par différents opérateurs (modems, programmes « client » comme Eudora, Skype, etc.) fait que personne n’a le droit de s’approprier le réseau.

En France, on avait le Minitel, mais sa situation propriétaire et monopolistique a énormément limité son potentiel d’innovation. Les débuts d’Internet ont été marqués à l’inverse par des idéaux libertaires : il s’agissait de créer un bien commun, de faire en sorte que tous puissent en profiter.

A des idéaux libertaires ou anarchistes, on a ajouté un zeste de collectivisme. L’information sur Internet est un bien qui ne s’épuise pas dans son usage collectif: cela contredit l’idée des économies de marché selon laquelle seule la propriété privée garantirait un usage des ressources raisonnable.

L’essor de l’idéologie libertaire explique en partie pourquoi il est si difficile, encore maintenant, de trouver un modèle économique pour l’Internet.

Il y a trois niveaux de douane susceptibles de rémunérer des investissements: le droit d’accès au contenu, le droit d’accès au code, et le droit d’accès au câble. L’innovation apportée par certaines inventions du web 2.0, comme les blogs, a été de supprimer les deux premières barrières.

Depuis 2001, ce n’est plus la question technologique qui domine, c’est la question de la participation, qui est une question sociale et politique.

Le filtrage des informations serait d’autant plus justifié que l’on se trouve en contexte d’incertitude. Quel est le rôle du jugement, de la responsabilité individuelle? Est-ce que l’on peut parler de sagesse collective sur Internet?

Internet d’aujourd’hui est devenu principalement un outil social de traitement automatisé d’une énorme quantité d’informations. Le web 2.0, ou web social, tient compte des préférences individuelles en les agrégeant: lorsqu’un internaute crée un lien, il met à disposition des autres une préférence individuelle.

Le web est d’un coté la réalisation d’un rêve de collecte d’information issu du libéralisme. Pourquoi le marché est-il si important selon Friedrich Hayek [figure de proue de la pensée économique libérale, ndlr])? Parce que le marché fixe un prix, et que le prix est l’indicateur d’informations éparpillées dans la société.

Aujourd’hui, le prix est un indicateur dépassé. On ne sait pas grand-chose d’un individu si l’on sait qu’il achète une bouteille à 3 euros, 5 euros ou 8 euros. L’agrégation d’informations utiles ne passe plus par le prix, mais par des systèmes de filtrage collaboratif des informations beaucoup plus sophistiqués. C’est d’ailleurs la transformation la plus intéressante du web depuis une dizaine d’années.

Il faut néanmoins être conscient de ce que les processus de formation de la sagesse collective varient énormément d’un système à l’autre. Prenez Google, prenez Wikipedia, c’est très différent: l’individu n’a qu’une influence très indirecte sur le PageRank en créant des liens, dont le poids est ensuite manipulé par des algorithmes qu’il ne contrôle point, alors qu’il peut intervenir directement sur un article de Wikipedia.

Lorsque Google a commencé, les internautes n’étaient pas conscients que le référencement pouvait être payant. C’est une intervention institutionnelle, une loi, aux Etats-Unis, qui a obligé les moteurs de recherche à séparer visuellement ce qui fait l’objet d’une promotion commerciale. Ces biais des systèmes sont mal connus, et je trouve que leur maîtrise devrait faire partie d’une éducation à leur usage.

Traditionnellement, la sociologie avait un rôle à jouer dans l’étude des comportements sociaux, et la philosophie s’impliquait dans l’étude du sens… Comment concevez-vous votre travail sur l’objet Internet?

Mon travail se situe dans la ligne directe de mes autres travaux d’épistémologue. Comment la connaissance est-elle produite, diffusée, stockée?

Il était pour moi impossible de rester indifférente à Internet. L’objet Internet n’a toutefois rien d’une reconversion: c’est ma façon d’étudier le design de la connaissance, qui est l’objet même de l’épistémologie.

Il reste difficile de faire passer Internet dans la culture académique, comme vous en avez également fait l’expérience, même si tous les universitaires utilisent les e-mails, Wikipedia, Facebook, Google, etc. Ces activités ne font pas partie de l’activité officielle d’un chercheur, alors que cela représente sans doute 80% de son activité réelle…

Dans le cadre d’un projet européen auquel je participe comme épistémologue, LiquidPublications [2], nous sommes en train de concevoir une nouvelle façon de produire la science, de valoriser toute l’activité d’un chercheur, en prenant en compte les outils sociaux du web 2.0.

La question, aujourd’hui, c’est que la plus grande partie de l’activité d’un chercheur n’est pas prise en compte dans l’évaluation de sa carrière scientifique.

C’est l’un des messages que j’essaye de faire passer au niveau européen, auprès de l’European Research Council, et même en France, où je collabore avec les projets du CNRS qui essayent de remettre en question les pratiques de diffusion de la recherche (comme le projet TGE Adonis [3].

Je pense qu’il faut passer d’une conception statique de la connaissance, incarnée aujourd’hui par l’article de recherche, à une conception dynamique, nécessairement plus collective.
URL source: http://www.rue89.com/innovation/2008/11/02/du-vin-au-web-20-comment-la-sagesse-collective-se-forme

Liens:
[1] http://www.laviedesidees.fr/Sagesse-en-reseaux-la-passion-d.html
[2] http://liquidpub.org/
[3] http://www.tge-adonis.fr

Sagesse en réseaux. Designing wisdom

An article on Collective Wisdom on the web published in English and French by the French magazine
La vie des idées.

You find the online text here

Designing Wisdom Through the Web: The Passion of Ranking

Draft. Do not Quote. Presented at the workshop on Collective Wisdom, Collège de France, Paris 22-23 May 2008.

Let me start with a rather trivial remark: Design matters. This triviality is rich of consequences for collective wisdom. This is the central claim I would like to defend through this paper. No matter how many people are involved in the production of a collective outcome – a decision, an action, a cognitive achievement etc. – the way in which their interactions are designed, what they may know and not know of each other, how they access the collective procedure, what path their actions follows and how it merges with the actions of others, affects the content of the outcome. Of course this is well known by policy makers, constitution writers and all those who participate into the institutional design of a democratic system, or any other system of rules that has to take into account the point of view of the many. But the claim may appear less evident – or at least in need of a more articulate justification - when it deals with the design of knowledge and the epistemic practices on the Web. That is because the Web has been mainly seen as a disruptive technology whose immediate effect was to blow up all the existing legitimate procedures of knowledge access, thus “empowering” its users with a new intellectual freedom, the liberty to produce, access and distribute content in a totally unregulated way. Still, methods of tapping into the wisdom of the crowds on the Web are many and much more clearly differentiated that it is usually acknowledged. In his book on the Wisdom of Crowds – probably the only shared piece of collective wisdom that we are able to attribute to each other as a background reading in this very interdisciplinary conference – James Surowiecki writes about the different designs for capturing collective wisdom: “in the end there is nothing about a futures market that makes it inherently smarter than, say, Google. These are all attempts to tap into the wisdom of the crowd, and that’s the reason they work”. Yet, sometimes the devil is in the details and the way in which the wisdom of crowds is captured makes a huge difference on its outcome and its impact on our cognitive life. The design question that is thus central when dealing with these systems is: How can people and computers be connected so that—collectively—they act more intelligently than any individuals, groups, or computers?

In this paper I will try to go through the details of some of the collective wisdom systems that are nowadays used on the Web. I will provide a brief “technical” description of the design that underlies each of them. Then, I will argue that these systems work because of their very special way of articulating (1) individual choices and collectively-filtered preferences on one hand and (2) human actions and computer processes on the other. I will then conclude by some epistemological remarks about the role of ranking in our epistemic practices, arguing that the success of the Web as an epistemic practice is due to its capacity to provide not so much a potentially infinite system of information storage, but a giant network of ranking and rating systems in which information is valued as long as it has been already filtered by other people. My modest epistemological prediction is that the Information Age is being replaced by a Reputation Age in which the reputation of an item – that is how others value and rate the item - is the only way we have to extract information about it. I see this passion of ranking in collective wisdom as such a central feature that I’m tempted to add it as a condition in the very illuminating list of conditions that James Surowiecki imposes on the characterisation of a wise crowd, that is:

1. diversity of opinion (each person should have some private information)
2. independence (people’s opinions are not determined by others)
3. decentralization (people are able to draw on local knowledge)
4. aggregation (presence of mechanisms that turn individual judgements in collective decisions)

5. presence of a rating device (each person should be able to produce a rating hierarchy, rely on past ranking systems and make – at least in some circumstances – his or her rating available to other persons)

I think that this last condition is particularly useful to understand the processes of collective intelligence that the Internet has made possible, although it is not limited to the Internet phenomenon. Of course, this opens the epistemological question of the epistemic value of these rankings, that it, to what extent their production and use by a group changes the ratio between truths and falsities produced by that group and, individually, how an awareness of rankings should affect a person’s beliefs. After all, rankings introduce a bias in judgement and the epistemic superiority of a biased judgement is in need of justification. Moreover, these rankings are the result of collective human registered activities with artificial devices. The control of the heuristics and techniques that underlie this dynamics of information may be out of sight or incomprehensible for the users who find themselves in the very vulnerable position of relying on external sources of information through a dynamic, machine-based channel of communication whose heuristics and biases are not under their control. For example, that companies used to pay to be included in search engines or gain a “preferred placement” was unknown to 60% of users[1] until the American Federal Trade Corporation wrote in 2002 a public recommendation asking to search engines companies to disclose paid link policies and clearly mark advertisements to avoid users’ confusion.

The epistemic status of these collectively produced rankings thus opens a series of epistemological questions:

1. Why do people trust these rankings and should they?

2. Why should we assume that the collective filtering of preferences produces wiser results on the Web?

3. What are the heuristics and biases of the aggregating systems on the Web that people should be aware of?

These questions include a descriptive as well as a normative perspective on the social epistemology of collective wisdom systems. A socio-epistemological approach to these questions - as the one I endorse - should try to elucidate both perspectives. Although this paper will explore more the descriptive side of the question, by showing the design of collective wisdom systems with their respective biases, let me introduce these examples by some general epistemological reflections that suggest also a possible line of answer to the normative issues. In my view, in an information-dense environment, where sources are in constant competition to get attention and the option of the direct verification of the information is simply not available at reasonable costs, evaluation and rankings are epistemic tools and cognitive practices that provide an inevitable shortcut to information. This is especially striking in contemporary informationally-overloaded societies, but I think it is a permanent feature of any extraction of information from a corpus of knowledge. There is no ideal knowledge that we can adjudicate without the access to previous evaluations and adjudications of others. And my modest epistemological prediction is that the higher is the uncertainty on the content of information, the stronger is the weight of the opinions of others in order to establish the quality of this content. This doesn’t make us more gullible. Our epistemic responsibility in dealing with these reputational devices is to be aware of the biases that the design of each of these devices incorporates, either for technical reasons or for sociological or institutional reasons. A detailed presentation of what sort of aggregation of individual choices the Internet makes available should be thus accompanied by an analysis of the possible biases that each of these systems carries in its design.

1. Collective intelligence out of individual choices

People - and other intelligent agents - often think better in groups and sometimes think in ways which would be simply impossible for isolated individuals. The Internet is surely an example of this. That is why the rise of the Internet created from the onset huge expectations about a possible “overcoming” of thought processes at the individual level, towards an emergence of a new – more powerful – form of technologically-mediated intelligence. A plethora of images and metaphors of the Internet as a super-intelligent agent thus invaded the literature on media studies – such as the Internet as an extended mind, a distributed digital consciousness, a higher-order intelligent being, etc…

Yet, the collective processes that make Internet such a powerful cognitive media are precisely an example of “collective intelligence” in the intended meaning of this workshop, that is, a mean of aggregation of individual choices and preferences. What Internet made possible though – and this was indeed spectacular - was a brand new form of aggregation that simply didn’t exist before its invention and diffusion around the world. In this sense, it provided a new tool for aggregating individual behaviours that may serve as a basis for rethinking other forms of institutions whose survival depends on combining in the appropriate way the views of the many.

1.1. The Internet and the Web

As I said in the introduction, the salient aspect of this new form of aggregation is a special way of articulating individual choices and collectively-filtered preferences through the technology of the Internet and, especially, of the World Wide Web. In this sense, it is useful to distinguish from the onset between the Internet as a networking phenomenon and the Web as a specific technology made possible by the existence of this new network. The Internet is a network whose beginnings go back to the Sixties, when American scientists at AT&T, Rand and MIT and the Defense Communication Agency started to think of an alternative model of transmitting information through a network. In the classical telephone system, when you call New York from your apartment in Paris, a circuit is open between you and the New York destination – roughly a copper line which physically connects the two destinations. The idea was thus to develop an alternative – “packet-switching” technology, by digitalizing conversations – that is – translating waves into bits, then chopping the result into packets which could flow independently through a network while giving the impression of a real-time connection on the other end. In the early Seventies the first decentralised network, Arpanet, was put in use that was able to transfer a message by spreading its chunks through the network and then reconstructing it at the end. By the mid Seventies, the first important application on the network, the mail, was created. What made this net such a powerful tool was its decentralised way of growing: Internet is a network of networks, which uses pre-existing wires (like telephone networks) to make computers communicate through a number of protocols (things like: IP/TCP) that are not proprietary: each new user can connect to the network by using these protocols. Each invention of an application, a mail system, a system of transfer of video, a digital phone system, can use the same protocols. Internet protocols are “commons”[2], and that was a boost to the growth of the network and the creativity of the applications using it. This is a crucial for the wisdom of the net. Without the political choice to keep these protocols free, the net would not have grown in a decentralised manner and the collaborative knowledge practices that it has realized would not have been possible. The World Wide Web, which is a much more recent invention, maintained the same philosophy of open protocols compatible with the Internet (like HTTP –hypertext transfer protocol or HTML- hypertext markup language). The Web is a service which operates through the Internet, a set of protocols and conventions that allows “pages” (i.e. a particular format of information that makes easy to write and read content) to be easily linked to each other, by the technique of hyperlink. It’s a visualization protocol that makes the display of information very simple. The growth of the Web is not the same thing as the growth of Internet. What made the Web grow so fast is that the creating a hyperlink doesn’t require any technical competence. The Web is an illustration of how an Internet application may flourish thanks to the openness of the protocols. And it is true that impact of IT on collective intelligence are due mostly to the Web.

1.2. The Web, collective memory and meta-memory

What makes the aggregation of individual preferences so special through the Web? For the history of culture, the Web is a major revolution on the storage, dissemination and retrieving of information. The major cultural revolutions in the history of culture have had an impact on the distribution of memory. The Web is one such revolution. Let’s see in what sense. The Web has often been compared to the invention of writing or printing. Both comparisons are valid. Writing, introduced at the end of the 4th millennium BCE in Mesopotamia, is an external memory device that makes possible the reorganization of intellectual life and the structuring of thoughts, neither of which are possible in oral cultures. With the introduction of writing, one part of our cognition “leaves” the brain to be distributed among external supports. The visual representation of a society’s knowledge makes it possible to both reorganize the knowledge in a more useful, more ‘logical’, way by using, for example, lists, tables, or genealogical trees, and to solidify it from one generation to the next. What’s more, the birth of “managerial” casts who oversee cultural memory, such as scribes, astrologists, and librarians, makes possible the organization of meta-memory, that is, the set of processes for accessing and recovering cultural memory.

Printing, introduced to our culture at the end of the 15th century, redistributes cultural memory, changing the configuration of the “informational pyramid” in the diffusion of knowledge. In what sense is the Web revolution comparable to the invention of writing and printing? In line with these two earlier revolutions, the Web increases the efficiency of recording, recovering, reproducing and distributing cultural memory. Like writing, the Web is an external memory device, although different in that it’s “active” in contrast to the passive nature of writing. Like printing, the Web is a device for redistributing the cultural memory in a population, although importantly different since it crucially modifies the costs and time of distribution. But unlike writing and printing, the Web presents a radical change in the conditions for accessing and recovering cultural memory with the introduction of new devices for managing meta-memory, i.e., the processes for accessing and recovering memory. Culture, to a large extent, consists in the conception, organization and institutionalization of an efficient meta-memory, i.e. a system of rules, practices and representations that allow us to usefully orient ourselves in the collective memory. A good part of our scholastic education consists in internalizing systems of meta-memory, classifications of style, rankings, etc.. chosen by our particular culture. For example, it’s important to know the basics of rhetoric in order to rapidly “classify” a line of verse as belonging to a certain style, and hence to a certain period, so as to be able to thus efficiently locate it from within the corpus of Italian literature. Meta-memory thus doesn’t serve only a cognitive function – to retrieve information from a corpus – but a social and epistemic function to provide an organization for this information in terms of various systems of classifications that embody the value of the “cultural lore” of that corpus. The way we retrieve information is an epistemic activity which allows us to access through the retrieving filters, how the culture autorities on a piece of information have classified and ranked it within that corpus. With the advent of technologies that automate the functions of accessing and recovering memory, such as search engines and knowledge management systems, meta-memory also becomes part of external memory: a cognitive function, central to the cultural organization of human societies, has become automated—another “piece” of cognition thus leaves our brain in order to be materialized through external supports. Returning to the example above, if I have in mind a line of poetic verse, say “Guido, i’vorrei...” but can recall neither the author nor the period, and am unable to classify the style, these days I can simply write the line of verse in the text window of a search engine and look at the results. The highly improbable combination of words in a line of verse makes possible a sufficiently relevant selection of information that yields among the first results the poem from which the line is taken (my search for this line using Google yielded 654 responses, the first ten of which contained the complete text from the poem in Dante’s Rime).

How is this meta-memory designed through the Web technology? What is unique on the Web is that the actions of the users leave a track on the system that is immediately reusable by it, like the trails that snails leave on the ground, which reveal to other snails the path they are following. The combination of the tracks of the different patterns of use may be easily displayed in a rank that informs and influence future preferences and actions of the users. The corpus of knowledge available on the Web – built and maintained by the individual behaviours of the users – is automatically filtered by systems that aggregate these behaviours in a ranking and make it available as filtered information to new, individual users. I will analyse different classes of meta-memory devices. These systems, although they both provide a selection of information that informs and influences users’ behaviour, are designed in a different way, a difference is worth taking notice of.

2. Collaborative filtering: wisdom out of algorithms

2.1. Knowledge Management Systems

Collaborative filtering is a way of making predictions about the preferences of a users based on the pattern of behaviour of many other users. It is mainly used for commercial purposes in web applications for e-business, although it has been extended to other domains. A well-known example of a system of collaborative filtering which I assume we are all familiar with, is Amazon.com : Amazon.com is a Web application, a knowledge management system which keeps track of users’ interactions with the systems and is designed to display correlations between patterns of activities in a way that informs users about other users’ preferences. The best known feature of this system is the one which associates different items to buy: “Customers who buy X buy also Y”. The originality of these systems is that the matching between X and Y is in a sense bottom-up (although the design of the appropriate thresholds of activities above which this correlation emerges are fixed by the information architecture of the system). The association between James Surowiecki’s book and Ian Ayer’s book Super Crunchers that you can find on the Amazon’s page for The Wisdom of Crowds has been produced automatically by an algorithm that aggregates the preferences of the users and makes the correlation emerge. This is a unique feature of these interactive systems, in which new categories are created by automatically transforming human actions into visible rankings. The collective wisdom of the system is due to a division of cognitive labour between the algorithms which compose and visualize the information, and the users who interact with the system. The classifications and rankings that are thus created aren’t based on previous cultural knowledge of habits and customs of users, but on the emergence of significant patterns of aggregated preferences through the individual interactions with the system. Of course, biases are possible within the system: the weights associated to each item to make it emerge are fixed in such a way that some items have more chances to be recommended that others. But given that the system is alimented by the repeated actions of the users, a too biased recommendation that couples items that users won’t buy together will not be replicated enough times to stabilize within the system.

2.2. PageRank

Another class of systems that realize meta-memory functions through artificial devices are search engines. As we all know by experience, search engines have been a major transformation of our epistemic practices and a profound cognitive revolution. The most remarkable innovation of these tools is due to the discovery of the structure of the Web at the beginning of this century[3]. The structure of the Web is that of a social network, and contains a lot of information about its users’ preferences and habits. The search engines of second generation, like Google, are able to exploit this structure in order to gain information about how knowledge is distributed throughout the world. Basically, the PageRank algorithm interprets a link from a page A to page B as a vote that page A expresses towards page B. But we’re not in democracy on the Web and votes do not have all the same weight. Votes that come from certain sites – called “hubs”- have much more weight than others, and reflect in a sense hierarchies of reputation that exist outside the Web. Roughly, a link from my homepage to Professor Elster’s page, weighs much less than a link to my page from that of Professor Elster. The Web is an “aristocratic” network – an expression that is used by the social network theorists – that is, a network in which “rich get richer” and the more links you receive the higher is the probability that you will receive even more. This disparity of weights creates a “reputational landscape” that informs the result of a query. The PageRank algorithm is nourished by the local knowledge and preferences of each individual user and it influences them by displaying a ranking of results that are interpreted as a hierarchy of relevance. Note that this system is NOT a knowledge management system: the PageRank algorithm doesn’t know anything about the particular pattern of activities of each individual: it doesn’t know how many times you and I go to the JSTOR website and doesn’t combine our navigation paths together. A “click” from a page to another is an opaque information for PageRank, whereas a link between two pages contains a lot of information about users’ knowledge that the system is able to extract. Still, the two systems are comparable from the point of view of the design of collective intelligence: neither requires any cooperation between agents in order to create a shared system of ranking. The “collaborative” aspect of the collective filtering is more in the hands of machines than of human agents[4]. The system exploits the information that human agents either unintentionally leave on the website by interacting with it (KM systems) or actively produce by putting a link from one page to another (search-engines): the result is collective, but the motivation is individual.

Biases of search engines have been a major subject of discussions, controversy and collective fears these years. As I’ve mentioned above, the refinement of the second-generation search engines such as Google has allowed at least to explicitly mark paid inclusions and preferred placements, but this needed a political intervention. Also, the “Mathiew effect” of aristocratic networks is notorious, and the risk of these tools is to give prominence to already powerful sites at the expense of others. The awareness of these biases should imply a refinement on the search practices also: for example, the more improbable is the string of keywords, the more relevant is the filtered result. Novices and learners should be instructed with even simple principles that make them less vulnerable to these biases.

3. Reputation systems: wisdom out of status anxiety

The collaborative filtering of information may require sometimes a more active participation to a community than what is needed in the examples above. In his work on Information Politics on the Web the sociologist Richard Rogers classifies web dynamics as “voluntaristic” or “non-voluntaristic” according to the respective role of human and machines in providing information feed-back for the users. Reputation systems are an example of a more “voluntaristic” web application than the ones seen above. A reputation system is a special kind of collaborative filtering algorithm that determines ratings for a collection of agents based on the opinions that these agents hold about each other. A reputation system collects, distributes, and aggregates feedback about participants’ past behaviour.

The best known and probably simplest reputation system of large impact on the Web is the system of auction sales at www.eBay.com . eBay allows commercial interactions among more than 125 millions of people around the world. People are buyers and sellers. Buyers place a bid on an item. If their bid is successful, they make the commercial transaction, then both (buyers and sellers) leave a feedback about the quality of that transaction. The different feedbacks are then aggregated by the system in a very simple feedback profile, where positive feedbacks and negative feedbacks plus some comments are displayed to the users. The reputation of the agent is thus a useful information in order to decide to pursue the transaction. Reputation has in this case a real, measurable, commercial value: in a market with a fragmented offer and very low information available on each offer, reputation becomes a crucial information in order to trust the seller. Sellers on eBay know very well the value of their good reputation in such a special business environment (no physical encounters, no chance to see and touch the item, vagueness about the normative framework of the transaction – if for example it is realized through two different countries, etc.), so there is a number of transactions at a very low cost whose objective is just to gain one more positive evaluation. The system creates a collective result forcing cooperation, that is, asking users to leave an evaluation at the end of the transaction and sanctioning them if they don’t comply. Without this active participation of the users, the system will be useless. Still, it is a special form of collaborative behaviour that doesn’t require any commitment to cooperation as a value. Non-cooperative users are sanctioned to different degrees: they can be negatively evaluated not only if the transaction isn’t good, but also if they do not participate into the evaluation process. Breaking the rules of e-bay may lead to the exclusion from the community. The design of wisdom thus comprises an active participation from the users for fear to be ostracized by the community (which would be seen as a loss of business opportunities). Biases are clearly possible here also. People invest in cheap transactions whose only aim is to gain reputational points. This is a bias one should be aware of and easily check: if a seller offers too many cheap items, he too concerned with his public image to be considered reliable.

Some reputational features are used also by non-commercial systems such as www.flickr.com. Flickr is a collaborative platform to share photos. For each picture, you can visualise how many users have added it among their favourite pictures and who they are.

Reputation systems differ from other systems of measurement of reputation that use citation analysis, like for example the Science Citation Index. These systems are in a sense reputation-based, given that they use scientometric techniques to measure the impact of a publication in terms of the number of citations in other publications. But they don’t require any active participation of the agents in order to obtain the measure of reputation.

4. Collaborative, open systems: wisdom out of cooperation

The collaborative filtering on the Web may be even more voluntaristic and human-based than the previous examples, while still necessitating a Web support to realize an intelligent outcome. Two are the most discussed cases of collaborative systems that owe their success to active human cooperation in filtering and revising the information made available: the Open Source communities of software development, like Linux, and the collective open content projects such as Wikipedia. In both cases, the filtering process is completely human-made: code or content is made available to a community which can filter it by correcting, editing of erasing it according to personal or shared standards of quality. I would say that these are communities of amateurs instead of experts, that is, people who love what they do and decide to share their knowledge for the sake of the community. Collective wisdom is thus created by individual human efforts that are aggregated in a common enterprise in which some norms of cooperation are shared.

I won’t discuss biases on Wikipedia: it is such a large topic that it could be the subject of another paper. Let me just mention that Larry Sanger, one of its founders, is promoting an alternative project, www.citizendum.org which endorses a policy of accreditation of its authors. Self-promotion, ideology, targeted attacks on reputation may of course act as biases in the selection of entries. But the fear of Wikipedia as a dangerous place of tendentious information has been disconfirmed by facts: thanks to its large size, Wikipedia is hugely differentiated in its topics and views, and it has been shown that its reliability is no less than that of the Encyclopedia Britannica[5].

Recommender systems: wisdom out of connoisseurship

Another class of systems is based on recommendations of connoisseurs in a particular domain. One of my favourite examples of wisdom created out of recommendations is the Music Genoma Project at www.pandora.com a sort of Web-based radio that works by aggregating thousands of descriptions and classifications of pieces of music produced by connoisseurs and matches these descriptions with the “tastes” of listeners (as they describe them). Then it broadcasts a selection of music pieces that correspond to what the listeners like to hear. And it works! Imagine how good would be to have a similar system that selects papers for you on the basis of recommendations of experts that match your tastes! Some recommender systems collect information from users by actively asking them to rate a number of items, or to express a preference between two items, or to create a list of items that they like. The system then compares the data to similar data collected from other users and displays the recommendation. It is basically a collaborative filtering technique with a more active component: people are asked to express their preferences, instead of just inferring their preferences from their behaviour, which makes a huge difference: it is well known in psychology that we are not so good in introspection and sometimes we consciously express preferences that are incoherent with our behaviour: If asked, I may express a preference for classical music, while if I keep a record of how many times I do listen to classical music compared other genres of music in a week, I realize that my preferences are quite different).

Conclusions

This long list of examples of Web tools for producing collective wisdom illustrates how fine-grained can be the choice of the design for aggregating individual choices and preferences. The differences in design that I have underlined end up in deep differences in the kind of collective communities that are generated by the IT. Sometimes the community is absent, as in the case of the Google users, who cannot be defined as a “community” in any interesting normative sense, sometimes the community is normatively demanding, as in the case of eBay, in which participation in the filtering process is needed for the survival of the community. If the new collective production of knowledge that the Web – and in particular the Web 2.0 – makes possible should serve as a laboratory for designing “better” collective procedures for the production of knowledge or of wise decisions, these differences should be taken into account.

But let me come back in the end with a more epistemological claim about what kind of knowledge is produced by these new tools. As I said at the beginning, these tools work insofar as they provide access to rankings of information, labelling procedures and evaluations. Even Wikipedia, which doesn’t display any explicit rating device, works on the following principle: if an entry has survived on the site – that is, it has not been erased by other wikipedians – it is worth reading it. This can be a too weak evaluative tool, and, as I said, discussion goes on these days on the opportunity to introduce more structured filtering devices on Wikipedia[6], but it is my opinion that the survival or even egalitarian projects like Wikipedia depends on their capacity to incorporate a ranking: the label Wikipedia in itself works already as a reputational cue that orients the choices of the users. Without the reputation of the label, the success of the project would be much more limited.

As I said at the beginning, the Web is not only a powerful reservoir of all sort of labelled and unlabelled information, but it is also a powerful reputational tool that introduces ranks, rating systems, weights and biases in the landscape of knowledge. Even in this information-dense world, knowledge without evaluation would be a sad desert landscape in which people would be stunned in front of an enormous and mute mass of information, as Bouvard et Pécuchet, the two heroes of Flaubert's famous novel, who decided to retire and to go through every known discipline without, in the end, being able to learn anything. An efficient knowledge system will inevitably grow by generating a variety of evaluative tools: that is how culture grows, how traditions are created. A cultural tradition is to begin with a labelling system of insiders and outsiders, of who stays on and who is lost in the magma of the past. The good news is that in the Web era this inevitable evaluation is made through new, collective tools that challenge the received views and develop and improve an innovative and democratic way of selection of knowledge. But there's no escape from the creation of a "canonical"—even if tentative and rapidly evolving—corpus of knowledge.

References

A. Clark (2003) Natural Born Cyborgs, Oxford University Press.

L. Lessig (2001) The Future of Ideas, Vintage, New York

G. Origgi (2007) “Wine epistemology: The role of reputation and rating systems in the world of wine”, in B. Smith (ed.) Questions of Taste, Oxford University Press.

G. Origgi (2007) « Un certain regard. Pour une épistémologie de la réputation », presented at the workshop La réputation, Fondazione Olivetti, Rome, April 2007.

G. Origgi (2008) Qu’est-ce que la confiance, VRIN, Paris.

R. Rogers (2004) Information Politics of the Web, MIT Press

L. Sanger (2007) “Who says we know: On the new Politics of knowledge” at www.edge.org

Taraborelli, D. (2008) “How the Web is changing the way we trust”, in: K. Waelbers, A. Briggle, P. Brey (Eds.), Current Issues in Computing and Philosophy, IOS Press, Amsterdam, 2008.

P. Thagard (2001). Internet epistemology: Contributions of new information technologies to scientific research. In K. Crowley, C. D. Schunn, and T. Okada, (Eds.) Designing for science: Implications from professional, instructional, and everyday science.Mawah, NJ: Erlbaum, 465-485.

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[1] Princeton Survey Research Associates, “A Matter of Trust: What Users Want from Websites”, Princeton, January 2002, at: http://www.consumerWebwatch.com/news/report1.pdf . The case is reported in R. Rogers (2004) Information Politics on the Web, MIT Press.

[2] Cf. on this point, L. Lessig (2001) The Fututre of Ideas, Vintage, New York.

[3] Kleinberg, J. (2001) “The Structure of the Web”, Science.

[4] Knowledge management systems like Amazon.com have some collaborative filtering features that need cooperation, like writing a review of a book or ranking a book with the five stars ranking system, but these aren’t essential to the functioning of the collaborative filtering process.

[5] Cf. “Internet Encyclopedias go head to head” Nature, 438, 15 December 2005.

[6] See. L. Sanger «”Who says we know. On the new politics of knowledge” on line at www.edge.org and my reply to him, G. Origgi “Why reputation matters”