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FILL IN THE GAP: a New Alliance for Social and Natural Sciences” – Análise de texto

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Fill in the Gap. A New Alliance for Social and Natural Sciences

PREENCHA A LACUNA: uma nova aliança para as ciências sociais e naturais

Tommaso VENTURINI, Médialab Sciences Po, France,  Pablo JENSEN and Bruno LATOUR (2015) – Institut Rhônalpin des Systèmes Complexes (IXXI) and Laboratoire de physique, de Lyon

 

 

Nos últimos anos, a mídia eletrônica trouxe uma revolução na rastreabilidade dos fenômenos sociais. Como partículas em uma câmara de bolhas, as trajetórias sociais deixam trilhas digitais (netnografia) que podem ser analisadas para obter uma compreensão mais profunda da vida coletiva. Para dar sentido a esses vestígios, é necessária uma colaboração renovada entre cientistas sociais e naturais. As atuais estratégias de pesquisa baseadas em modelos micro-macro não são adequadas para desenvolver a complexidade da existência coletiva e que a prioridade deve ser o desenvolvimento de novas ferramentas formais para explorar a riqueza de dados digitais. Simulações, grandes volumes de dados (Big-Data), Ciências sociais, Micro Macro, Política, Modelagem

Netnografia: rastreio das trilhas deixadas por atores, objetos e fenômenos digitais. Rastrear o poder dos fluxos ao invés dos fluxos do poder (CASTELLS)

1.1 In the last decade, the spread of digital technologies has flooded the study of social phenomena with more data than ever dreamed of. Like rural countries pushed to a sudden industrialization by the global economy, the social sciences entered their age of abundance abruptly and with little preparation. Seeking help to handle their sudden fortune, social scientists turned to their colleagues in the natural sciences. Alas, the persistence of old habits has so far prevented researchers from taking full advantage of this alliance.

As Tecnologias digitais (TD) forneceram muitos dados às ciências sociais, que se aliaram aos colegas das ciências naturais, mas velhos hábitos evitaram a obtenção de vantagem significativa dessa aliança.

1.2 So far, the collaboration between natural and social scientists has invested vast efforts on modeling the emergence of collective phenomena from individual interactions, particularly through agent-based models (Cho 2009; Castellano et al. 2009; see also the very popular web site http://www2.econ.iastate.edu/tesfatsi/ace.htm and the special issues of PNAS, 99[suppl. 3] and American Journal of Sociology 110[4]). Applied to urban segregation (Schelling 1971), business locations (Krugman 1996), epidemics and cultural trends (Bouchaud 2012) and many other collective phenomena (cfr. most articles published in this journal), these models may range from simple ‘toy simulations’ to sophisticated systems based on empirical data (for example Broeck et al. 2011). Most of them, however, partake of the same conceptual approach in which individuals are taken as discrete and interchangeable ‘social atoms’ (Buchanan 2007) out of which social structures emerge as macroscopic characteristics (viscosity, solidity…) emerge from atomic interactions in statistical physics (Bandini et al. 2009).

Cientistas sociais e naturais investiram muito esforço na modelagem da emergência do coletiva a partir da percepção individual, mas consideraram os indivíduos como “átomos sociais” dos quais emergiriam as estruturas sociais com as mesmas características (macroscópicas).

1.3 In the past, this strategy found its rationale in the methodological discontinuity that characterized the social sciences. As long as the divide between qualitative and quantitative methods cast a blind spot between situated observations and aggregated indicators, little was the chance to reconstruct empirically the continuity of collective existence. In the shortage of data on the dynamics of folding and unfolding that determine both local interactions and global structures, simulations were a sensible way to investigate the so-called micro-macro link (Archer 1995; Giddens 1984). When the access to empirical data is too expensive, models allow at least to examine the logical consequences of the theoretical assumptions made at the local level. In the artificial worlds created by micro-macro models, researchers can play with the actors’ features or the interactions’ rules to produce a variety of patterns observed at the global level. As one famous economist puts it, simulations “provide fully articulated, artificial economic systems that can serve as laboratories in which policies that would be prohibitively expensive to experiment with in actual economics can be tested out at much lower cost.” (Lucas 1981, p. 271).

Passado: descontinuidade metodológica que caracterizava as ciências sociais (divisão, dicotomia à métodos qualitativos e quantitativos). Na falta de dados empíricos (muito caros) para determinar aproximações/afastamentos entre interações locais e estruturas globais, utilizaram-se simulações (modelos) artificiais (atores, regras, simulações para investigar o link micro-macro (Archer 1995; Giddens, 1984).

1.4 Micro-macro models, however, have serious methodological and political problems. From a methodological viewpoint, most simulations work only at the price of simplifying the properties of micro-agents, the rules of interaction and the nature of macro-structures so that they conveniently fit each other. A little bit like Descartes’ followers who explained the acidity of lemons by postulating the existence of ‘lemon atoms’ with tiny pricking needles (Hoddeson 1992). In the absence of empirical confirmation, social models tend to rely exclusively on internal coherence and are particularly vulnerable to the ‘confirmatory bias’ (Nickerson 1998; Rabin & Schrag 1999). “We opt for deductive verification of our claims in order to achieve clarity, rigour and certainty. But to get it we have tied the results to very special circumstances; the problem is how to validate them outside” (Cartwright 1999, p. 229).

Os modelos micro-macro têm problemas metodológicos: a maioria das simulações funciona aoo custo da simplificação das propriedades dos atores, regras, etc. Na ausência de confirmação empírica, modelos sociais tendem a depender exclusivamente da coerência interna e são particularmente vulneráveis ao “viés confirmatório” (Nickerson, 1998; Rabin & Schrag, 1999). “Para obter clareza, rigor e certeza, vinculamos os resultados a circunstâncias muito especiais, o problema é como validá-los” (Cartwright, 1999, p. 229)

1.5 From a political viewpoint, micro-macro models assume by construction that agents at the local level are incapable to understand and control the phenomena at the global level. Only the modelers can observe collective phenomena. Most simulations assume that only “human beings external to those involved – scholars and public officials – are able to analyze the situation, ascertain why counterproductive outcomes are reached, and posit what changes in the rules-in-use will enable participants to improve outcomes. Then, external officials are expected to impose an optimal set of rules on those individuals involved. It is assumed that the momentum for change must come from outside the situation rather than from the self-reflection and creativity of those within a situation to restructure their own patterns of interaction” (Ostrom 2010, p. 648). Ironically, a supposedly “bottom-up” approach (Epstein & Axtell 1996) leads to “top-down” social politics!

Modelos micro-macro: assumem que agentes locais são incapazes de compreender e controlar os fenômenos a nível global. Somente modeladores externos podem analisar a situação e impor um conjunto de regras para testar os resultados: o impulso para a mudança deve vir de fora da situação e não da auto-reflexão e criatividade dos próprios envolvidos.”(Ostrom 2010, pág. 648). Uma abordagem   “de baixo para cima” (bottom-up) (Epstein & Axtell 1996) leva a políticas sociais “de cima para baixo” (up-down)

1.6 The methodological and political difficulties of micro-macro models have been highlighted, for example, in the case of the so-called ‘tragedy of the commons’ (Hardin 1968). In these situations, personal interest pushes actors to overuse a shared good (a common pasture for example) to the detriment of the community. Assuming the existence of atomic agents each with its individually defined interest (as required by game theory), most of this literature cannot but confirm the overexploitation of the common good. However, empirical work conducted by Elinor Ostrom (1990) has shown that human cooperation can often (but not always) find arrangements to successfully manage the commons. Social simulations fail to obtain these arrangements, because they disregard the subtle mechanisms that govern the establishment of trust needed for cooperation. As shown by Ostrom, common standards, family ties, reputation and even facial expressions are crucial to obtain social cooperation. Impossible to anticipate through conceptual models, these factors can only be revealed by empirical observation.

Isso gera casos onde o interesse pessoal empurra os atores (agentes atômicos com interesses individuais) ao abuso de um bem compartilhado em detrimento da comunidade, ocorrendo sobreexploração do bem comum. Simulações sociais não conseguem obter arranjos baseados em cooperação porque ignoram os mecanismos sutis que regem o estabelecimento da confiança necessária para a cooperação: esses mecanismos só podem ser revelados pela observação empírica.

1.7 Ethnographic studies such as Ostrom’s, however, can expose the failures of micro-macro models, but not replace them. Relying on direct observation, qualitative researches can examine a number of situated exchanges but cannot follow how thousands of such interactions fold in the fabric of collective trust. And this is where the digital deluge may turn into a blessing.

Estudos etnográficos podem expor as falhas dos modelos micro-macro, mas não substituí-los. Já a observação direta, pesquisas qualitativas podem examinar uma série de trocas situadas, mas não podem seguir grandes fluxos de interações. Nesse ponto o dilúvio digital (registros) pode se transformar em uma benção.

1.8 The most interesting feature of digital media is that everything that they mediate becomes potentially traceable and often actually traced (Rogers 2013). Such traceability creates data that are as rich/thick as those collected by ethnographic techniques but covering much larger populations. Everyday new public and private archives are swallowed by computers memories, economic transactions migrate online, social networks root in the Web and the more this happens, the more traces become available on the collective dynamics that used to be hidden by the quali-quantitative divide (Latour et al. 2012). Since digital traces brought it to light, the continuum between local exchanges and global trends revealed much more interesting and rich than its extremes. Social existence does not jump from micro to macro and neither should social sciences. Structures do not pop up from interactions as rabbits from an illusionist’s hat. They are constructed and maintained by the relentless work of connecting and folding that (sometimes) leads to stronger, wider and longer lasting ‘associations’ (Latour 2005), as exemplified by studies of memes spreading (Leskovec 2009,http://memetracker.org); fame in the blogosphere (Cardon et al. 2011); migrant communities (Social Science Information, special issue 51:4, 2012; also http://www.e-diasporas.fr); manga styles (Manovich 2012); scientific paradigms (Chavalarias & Cointent 2009Börner 2010); open source collaboration (Heller & Marschner 2011), international negotiations (Venturini et al. 2014); lexical trends in history of literature (Michel et al. 2011); law amendments (http://www.lafabriquedelaloi.fr); Wikipedia controversies (Borra et al. 2014; also http://contropedia.net).

A mais interessante caracerísica da mídia digital é que tudo o que é mediado por ela torna-se potencialmente rastreável (Rogers 2013), criando dados tão ricos quanto aqueles coletados por técnicas etnográficas, mas populações muito maiores. Arquivos públicos, privados, transações pessoais, sociais e econômicas migram online e quanto mais isso acontece, mais traços ficam disponíveis na dinâmica coletiva que costumava ser escondida pela divisão quali-quantitativa ( Latour et al., 2012). Como os traços digitais o levaram à luz, o contínuo entre trocas locais/globais é mais rico e interessante que os extremos. Estruturas são construídas e mantidas pelo implacável trabalho de conexão/dobradura que (às vezes) leva a “associações” mais fortes, amplas e duradouras (Latour 2005). Ex,: memes, fama na blogosfera, comunidades de migrantes, estilos de mangá, paradigmas científicos, colaboração de código aberto, negociações internacionais, tendências lexicais da história da literatura, emendas de lei, controvérsias da Wikipedia

1.9 An example of how digital data can renew modeling can be found in opinion dynamics, one of the most popular subjects of social simulations. Several hundreds of papers have been published on this topic and some of them are among the most cited JASSS references. Their proponents argue that, despite their simplicity, these models have managed to produce a surprising variety of patterns when changing the details of the models or their parameters. However, these simulations have not yet succeeded to connect in any significant way to real-world behaviors (Sobkowicz 2009). These models use “thin concepts” which are homonymous with everyday concepts (‘opinion’), but “little of their behaviour from the real world is imported into the model” (Cartwright 1999). No surprise that “the impact of JASSS is higher in computer sciences, physics and ecology than it is in the social sciences, even though JASSS-indexed articles tend to be more concerned with social science-related topics” (Squazzoni & Casnici 2013).

Um exemplo de como os dados digitais podem renovar o modelo pode ser encontrado na dinâmica da opinião (simulações sociais). Tema popular das simulações sociais que, apesar de conseguir produzir uma variedade surpreendente de padrões ao mudar os detalhes/parâmetros dos modelos, ainda não conseguiu se conectar de maneira significativa aos comportamentos do mundo real (Sobkowicz 2009).

1.10 Thanks to the growing traceability of online discussions, it is now possible to describe in more realistic ways how people change opinion. For example, instead of assuming that each node influences all of its neighbors with the same probability, one would learn that the likelihood to adopt an opinion is higher “when participants receive social reinforcement from multiple neighbors in the social network” (Centola 2010). This leads to a “farther and faster spread across clustered-lattice networks than across corresponding random networks”, contrary to what most simulations suggested. In another online experiment tracing in detail the spread diet diaries, Centola (2011) showed that neighbors that are similar to oneself are much more influential, whereas most simulations assumed that each tie has equal weight in the diffusion process.

Graças à crescente rastreabilidade on-line, agora é possível descrever de maneira mais realista como as pessoas mudam a opinião. Em vez de assumir que cada nó influencia todos os seus vizinhos com a mesma probabilidade, aprenderia que a probabilidade de adotar uma opinião é maior “quando os participantes recebem reforço social de múltiplos vizinhos na rede social” (Centola 2010)ao contrário do que a maioria das simulações sugeriu ou que mostrou que os vizinhos que são semelhantes a si mesmos são muito mais influentes, enquanto a maioria das simulações supõe que cada empate tem o mesmo peso na difusão. Centola (2011)

1.11 Simulating the emergence of macro structures from micro interactions has never been an optimal research strategy, neither methodologically nor politically. Its main justification the possibility to bypass the supposed micro/macro divide lost much of its interest since the advent of digital media. Empirical studies show that, contrarily to what most social simulations assume, collective action does not originate at the micro level of individual atoms and does not end up in a macro level of stable structures. Instead, actions distribute in intricate and heterogeneous networks than fold and deploy creating differences but not discontinuities.

Simular macro-estruturas a partir de interações micro nunca foi uma estratégia de pesquisa otimizada, perdendo interesse desde o advento da mídia digital. Estudos empíricos mostram que, contrariamente ao que a maioria das simulações sociais assumem, a ação coletiva não se origina no nível micro, de átomos individuais. Em vez disso, as ações distribuem em redes intrincadas e heterogêneas, criando diferenças, mas não descontinuidades.

1.12 Digital traceability has transformed the context of the collaboration between social and natural scientists ant its agenda should change accordingly. The problem is not anymore to simulate data that would be too expensive to collect. The problem is to handle an avalanche of traces whose magnitude and diversity is unprecedented for the social sciences For the first time in their history, social scientists have continuous information about their objects and the last thing they need are models that break them in micro/macro oppositions.

A rastreabilidade digital transformou o contexto da colaboração entre cientistas sociais e naturais. Ao invés de coletar/simular dados, o problema é lidar com uma avalancha de traços cuja magnitude e diversidade são sem precedentes para as ciências sociais. Pela primeira vez em sua história, os cientistas sociais têm informações contínuas sobre seus objetos e não precisam mais de modelos que os quebram em pposições micro-macro.

1.13 This does not mean, of course, that the modeling tradition of natural sciences ceases to be relevant for the study of collective life. Quite the contrary! Such experience is crucial to develop the new methods necessary to handle larger and more diverse datasets. At the beginning of the 19th century natural and social scientists developed together a new discipline, “statistics”, that helped them to interpret the new data available at that time (Hacking 1990Desrosières 2002). Today, the advent of digital data poses a similar challenge and calls for a similar alliance. Micro-macro models have run their course. The time is now to develop the formal techniques necessary to unfold the origami of collective existence and this should be the aim of the renewed alliance between the social and natural sciences. For the next few years, at least, efforts should be shifted from simulating to mapping, from simple explanations to complex observations.

A tradição de modelagem das ciências naturais continua relevante para o estudo da vida coletiva, sendo a modelagem crucial para desenvolver métodos necessários para lidar com conjuntos de dados maiores/mais diversos. No início do século 19 cientistas naturais e sociais desenvolveram juntos uma nova disciplina, “estatística”, que os ajudou a interpretar os novos dados disponíveis naquela época (Hacking 1990; Desrosières 2002). Hoje, o advento dos dados digitais representa um desafio semelhante e exige uma aliança similar: desenvolver as técnicas formais necessárias para desenvolver o origami da existência coletiva e este deve ser o objetivo da aliança renovada entre as ciências sociais e naturais. Deve-se evoluir da simulação para o mapeamento, desde explicações simples até observações complexas.

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