Social systems from simulation to observation


Published on

Paper delivered during the Académie Internationale de Philosophie des Sciences annual congress, The Legacy of A.M. Turing, Urbino September 25-27, 2012.

Authors: Lella Mazzoli, Fabio Giglietto

The cognition-computing short circuit, one of the most important legacies of Alan Turing’s work, is still affecting today both neuroscience and computer science. Starting from the proposals formulated by the British logician and mathematician, this paper underlines the rarely studied impact his ideas have had on the study of society. Social systems theories on the one hand and agent-based simulations on the other hand, have pinpointed once more the traditional sociological dualism between macro and micro-sociology. However, the advent of ‘’Big Data’ has paved the way to new techniques of investigation based on the study of new types of data, such as conversations taking place on popular web sites like Twitter and Facebook. Thanks to these techniques, we can go beyond simulation and observe the operation within the social “black box” in the same way as neuronal functional magnetic resonance imaging (fMRI) does as regards to the brain. This paper discusses the potential and limitations of these new methods of sociological investigation and their spillover effects on the theoretical development of the discipline.

Published in: Technology, Business
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Can machines think? With this question, Alan Turing opens its famous paper describing the imitation game, better known as the Turing test.Behind this apparently naive question, lies a suggestion, spreading all over the British logician and mathematician’s works, which inextricably links neuroscience fortunes to computer sciences fortunes. In a sort of logical short circuit, on the one hand machines are built according to the model of brain function, on the other hand the cognitive development is described according to the metaphor of information processing and computing (Brooks, Hassabis, Bray, & Shashua, 2012). Much has been written about the impact of this circular relationship between mind and computer, while not so much attention has been placed on the impact of this same computing metaphor on the social working models and, more generally, on groups of people. The aim of this paper is to investigate, from this less studied perspective, Alan Turing’s legacy according to the suggestions offered by the author, also in the light of the new investigation methods which, as regards both the brain functioning and the society, enable and promise to observe the inner working of the “biological black box”.
  • We refer, for the brain aspect, to neuronal functional magnetic resonance imaging (fMRI) techniques and, for the society aspect, to the possibility of analysing the operations of social systems through the study of conversation traces left on social networks such as Twitter or Facebook. These new techniques of investigation promise to open up a new era in the field of neuroscience and in social science (Lazer et al., 2009) making it possible, for the very first time in history, to watch the inner workings of these systems. In both cases, through special techniques, we get images representing the function of the system (neuronal or communicative connections) and displays of its elements (neurons or social actions and communications) activity. It is not possible to check the accuracy nor the likelihood of these representations for what they want to represent because in both cases they tend to make visible something which was not so and that most probably can hardly be seen in the future without some form of mediation or observation tool. For this reason we do not know how much those imitations correspond to the original, but what really matters - as Turing understood anticipating a few constructivist opinions - is what the observer (Foerster, 1987) thinks. To this regard, Turing’s meditations about cognition and computing, often criticised and dismissed as behaviourism, look surprisingly up-to-date.
  • Among the most famous criticism of note is Searle’s Chinese room (1984). According to this author, even if a machine would pass a Turing test, this would not mean that the machine shows intelligent behaviour, but eventually that it can manipulate symbols necessary to simulate this competence. The differences between Searle’s opinion and Turing’s one, can be construed by distinguishing between first and second order cybernetics. While Turing’s view refers to a human observer for recognizing and determining a machine's ability to exhibit intelligent behaviour, Searle considers intelligent behaviour as an inner characteristic of a system. Although less recent, Turing’s suggestion looks more similar to the second-order cybernetics idea suggested by Heinz von Foerster.The first aims at understanding how neural-cognition works, the latter aims at building “intelligent” machines. It is not by chance that on this topic no agreement could be reached by the scientists participating at the Dartmouth famous conference in 1956, which ended up in considering artificial intelligence as a discipline, but which also marked a separation between the two souls that had lived together within the cybernetic movement (Heims, 1994). On the one hand the engineers who, believing they had made much progress in computer programming simulating human intelligent behaviour, preferred to continue on the basis of an operating definition of intelligence. On the other hand, the philosophers, psychologists and neurophysiologists who decided to pursue their research into the human mind as much was still to be learnt about the function of the human nervous system (Umpleby, 2003). However, this separation has not fully divided the study of cognition from the development of computer science.
  • As far as sociology is concerned, there are a number of examples of this sometimes fruitful, sometimes misleading contamination. This is especially evident in approaches based on the idea of society as a system made up of elements and relationships among those elements. These approaches, as well as the studies of artificial intelligence both belong to the cybernetics movement.  Top-down approaches The idea of studying society as a system made of different parts related with each other dates back to well before the XX century, and it was first formulated by the Greek philosopher Aristotle, who defined society as a system in the metaphor about the biological organism. Without going back so far in time, it is clear that the idea of the social system and the use of the word itself developed with the advent of sociology. The relationship between the parts and the whole, connected to the development of the idea of work differentiation is at the basis of the theory expressed at the beginning of the XIX century by Claude Saint-Simon. His pupil August Comte developed this same idea. Among the precursors, we cannot but mention the English philosopher Herbert Spencer and his description of society as a super-organic entity separated from the parts or the individuals composing it and to the consequence that society is characterized by an evolutionary process similar to the one Darwin had described in the Origin of Species in relation to the natural world.Vilfredo Pareto was among the first to consider the possibility of studying society as a whole made up of interdependent parts. Durkheim himself, defining society as an emergent reality that can assume normal or pathological status, can certainly be numbered among the authors who introduced some of the key ideas that laid the basis for social system theories. Between the XIX and the XX centuries, Radcliffe-Brown and Bronislaw Malinowski developed two theories commonly regarded as the forerunners of functionalism, based respectively on a mechanistic perspective grounded on the role of institutions, and on an emergentistic perspective where the function of the social is defined according to the biological needs of individuals and mediated by culture.In the light of these authors, during the last seventy years, Talcott Parsons and NiklasLuhmann formulated their respective theories. The theory of social system developed by Parsons and that of social systems conceived by Luhmann are commonly considered the most important applications of cybernetics principles, general theory of the systems and second order cybernetics to the study of society as a system or as a network of social systems.The idea that society can be studied as a whole of elements (individuals, actions or communications) related to each other is not a new one though, but it is interesting to note how it has developed and strengthened in the last years at the same time as the spreading of the studies about artificial intelligence. It is not difficult to notice the contact points between Marvin Minsky’s connectionist approach and these theories about society as networks of actions or communications observing each other. Bottom-up approaches Nevertheless, whole-parts approaches of a systemic and cybernetic type are not the only one originating from the thoughts and suggestions offered by Alan Turing. On the opposite end of this ideal spectrum, where at one end we can see these macro-sociological approaches inspired by cybernetic theories and metaphors of the mind, there are in fact all those studies based on the simulation of social subjects’ behaviour through informational techniques. We are referring to agent-based simulation used not only in sociology, but also widely adopted by economic studies about consumer behaviour. From the observation of an individual behaviour, the researcher builds a (necessarily) simplified model simulating actions and reactions. With this technique one can, for instance, simulate the behaviour of an individual in different contexts, derive insights into the relationship between context and actions, and create simulations of possible scenarios. Even starting from simple rules of interaction, as demonstrated by the famous case of Langton’s ants, the functioning of the system can show emerging properties that are difficult to explain starting from the working rules of the single elements.From a sociological point of view, even more interesting are multi-agent-based simulation models. In these models, agents’ behaviour is affected not only by the context in which they are placed, but also by the behaviour of other agents that can work with similar or different rules. Multi-agent simulation systems are regularly used to study the impact on the automobile traffic flow of a new road system or on the stock market trading flow.These approaches therefore tend to create models that, provided a certain level of simplification, - what matters here is the result – simulate the behaviour of a social system starting from the function of its single elements. Thus, this is a “bottom-up” approach, compared to the “top-down” approach supported by the systemic theories of society discussed above. We have simplified but useful in practice models on the one hand – somewhat reminding to the weak AI approach - and theories claiming universal scope, difficult to use in practice or as empirical research support.The shared roots of top-down and bottom-up approaches are also confirmed by the attempts to crossbreed these approaches suggesting agent-based models inspired by social system theories. We go from simulations based on the descriptions of economic behaviour under economic shortage as suggested by Luhmann (Fleischmann, 2005) to models aiming at reproducing situations of double contingency (Michael Barber, 2006) to finish with the definition of anticipatory systems by LoetLeydesdorff (2009). Notwithstanding these attempts, a gap remains between theories with universal scope and simulation models. The traditional dualism between micro and macro sociology (Alexander, 1987; Boudon, 1980; Mazzoli, 2001) seems therefore suggested again also as regards these innovative approaches inspired by the theories of mind and by artificial intelligence.In this context, the advent of Internet comes in. The availability of an inexpensive global network of communication has had, and is still having, an extraordinary impact on numerous aspects of daily life. It is not by chance that the metaphor of the network is considered a specific characteristic of contemporary society (Castells, 1996). The equal nature of this network has given rise, mainly further to the extraordinary success of so-called social media, to a phenomenon of progressive re-arrangement of the possibilities of communication and the power dynamics related to it (Jenkins, 2006). A condition of permanent connection which opens up to new forms of reflexivity, both at individual level and at society level, where we can see overcoming of that modernity which is often matched by the development and spread of printing and other means of mass communication (BocciaArtieri, 2012). However, the same study of society necessarily entails a reflexivity exercise, since research and study are an integral part of the subject under study (Luhmann & De Giorgi, 2000). This is why we can reasonably expect that such significant changes in society will imply changes in the status of the discipline itself.
  • The first change we can notice concerns research methodologies.  As mentioned in the previous paragraph, over the last few years we have seen the spread on the net of the use of platforms commonly known as social media. Social media are a varied category of Internet services and sites inspired by the advent of Web 2.0 (O’Reilly, 2007) and sharing the fact that the contents of these services are generated by the users of the site itself. Social network sites based on visible profiles, interconnected and navigable, (danah m. boyd & Ellison, 2008) belong to this category as well as the sites based on content sharing (videos, photos, etc.). The most popular social network sites are Facebook and Twitter. While among the most used content sharing sites are YouTube and Flickr!. Hundreds of millions of users (Facebook, 2012) every day exchange contents and comments through such and similar platforms. These conversations come up beside - but do not replace - public and private conversations and those conversations mediated or not that people usually have and have always had. However, some special properties of these conversations make them especially interesting from a social research point of view. The qualities of persistence, replicability, scalability and searchability (danahboyd, 2008) of these digital conversations allow to observe and analyze the contents and the structure of these conversations, in a way that was not possible to do before. Understanding the dynamics of information-spread on a social network (Hughes & Palen, 2009) or the use of these networks for the organization and coordination of the single individual activities (Shirky, 2008) or the engineering of political consent (Dahlgren, 2005), represent only some of the many possible examples of the application of studies based on user-generated content analysis.
  • If empirical research of a sociological nature is traditionally based on data obtained further to specific urges (focus groups, interviews, questionnaires), general data analysis by users on the web pertains to the tradition of content analysis (especially mass media generated content) (Krippendorff, 2004). Thus, this research differentiate from interviews and focus group content analysis in so far data is spontaneously generated. Compared to mass media-generated content analysis (reviews articles or radio/TV shows) there are similarities but also substantial differences. The similarity concerns the fact that in both cases we have to do with a level of emerging communication settled in the course of time. The difference concerns the fact that available content is no longer produced by a select group of individuals (Giglietto, 2009) and are not subject to filtering before publishing (Shirky, 2008).
  • Applications of simulation techniques also reveal important differences. Wherever simulation techniques include the implementation of social system function models, user-generated content analysis through social media platforms let us directly watch how these systems work. The link between users and shared objects (photos, videos, conversations) creates networks that can be visualized and studied with social network analysis and examined by content analysis methods. If on the one hand the great quantity of these contents represents a valuable resource, on the other hand it asks the sociologist to face up the need of mastering analysis techniques and data bases for which he/she may have not received adequate training (Giglietto & Rossi, 2012).Not surprisingly – especially if we consider that social media represent only the tip of the iceberg of a phenomenon including the traces our digital portable and connected devices tend to leave (from our phone conversations to travelling movements) – a few researchers suggested to lay down a manifesto of a new discipline known as Computational Social Science (Lazer et al., 2009). It is a growing study corpus, often promoted by researchers with a scientific background, who made it possible, among other things, to demonstrate the hypothesis formulated by Granovetter (1973) about the importance of weak ties for social mobility (Giles, 2012). Controversial (Gayo-Avello, 2012), but not less interesting, are also the studies into forecasting future social situations – box office revenues (Asur & Huberman, 2010), stock market trends (Bollen, Mao, & Zeng, 2011) and electoral results (Giglietto, 2012; Tumasjan, Sprenger, Sandner, & Welpe, 2010) – based on available data generated by users on social media.
  • Although potentials and limits – privacy, data representativeness compared to population -, lack of transparency of platforms as regards researchers’ access to data, research replicability – of these new research methods (BocciaArtieri, Giglietto, & Rossi, 2012; boyd & Crawford, 2011) have been addressed by social scientists, the theoretical effects of this technological innovation are not fully explored. Only recently, in fact, sociologists such as Latour have shown some interest regarding the impact of the large availability of interconnected data navigable through profiles spontaneously produced by hundreds of millions of individuals in the world, on the traditional fracture between micro and macro approaches to the study of society (Latour, Jensen, Venturini, Grauwin, & Boullier, 2012). Latour, resuming Gabriel Tarde’s monads definition, arrives at conjecturing a sociology collapsed on one single level. According to the French sociologist and his colleagues, the groups and the emerging realities traditionally considered the fruit of the interaction of groups of individuals are just individualities different from that of the subjects. In both cases they are monads related one another by the navigation path among the knots in the network. If it is true that an individual is characterised also by his belonging to a certain group, it is also true that such individual’s belonging characterises the group itself. Unlike what we commonly think, if the group is considered an emerging reality compared to the individuals constituting it, according to Latour, it is actually the directionality of the navigation, that determines which of the two realities logically includes the other one.Even without referring to this interesting proposal of revisiting Tarde’s theory, it is clear that today - more than ever before - it is possible to display social ties and networks of conversations. When we meet theories – such as social systems theory – describing society as a set of interconnected networks, we cannot but think of the similarity between displays obtained by social network analysis techniques. It is not by chance that these similarities emerges more promptly when we compare theories of society that define social systems as emerging phenomena regarding the individuals involved. And it is no accident that these research methodologies are exposed to criticism (Ardigò, 1988; Donati, 1992) similar to those addressed to the theories of social systems.
  • Can the display or the analysis of a conversation network, always detached from single individuals taking part to that conversation, help us fully understand the phenomenon under study?Looking at it closely, this question is not so different to the one we started out with: can machines think? The big difference is that in this case we are not talking about simulations any more but about observations of the system operations. What is the relationship between the images obtained by neuronal magnetic resonance and the thought? What is the relationship between the displays of networks of communications and the relationship between individuals and society?
  • Sociology therefore seems to be faced by a dilemma similar to the one faced by the diversified group of scholars who in Dartmouth tried to get to a common definition of Artificial Intelligence. On the one hand, we see the development of a new discipline, computational social science, strongly supported by researchers with mainly scientific backgrounds and journals such as Nature or Science. On the other hand, there are social researchers seeing these approaches as an over-the-top way forward not sufficiently supported by an adequate understanding of the functional mechanism of society. Although collaboration would be the road to follow, experience of artificial intelligence history highlights all the difficulties of this vital process.
  • Social systems from simulation to observation

    1. 1. Académie Internationale de Philosophie des Sciences annual congress, The Legacy of A.M. Turing, Urbino 25-27 Settembre 2012SOCIAL SYSTEMS: FROM SIMULATION TO OBSERVATIONLella Mazzoli []Fabio [.] Giglietto []Deparment of Communication Studies and Human Sciences | Università di Urbino Carlo Bo
    2. 2. Summary The legacy of Alan Turing in social sciences; Cognition, AI and simulation of social systems  Top-down approaches  Bottom-up approaches Form simulation to observation Conclusions
    3. 3. The cognition-computing short circuit
    4. 4. Inside the “biological blackbox”
    5. 5. Can machines think? PERMANENT LINK TO THIS COMIC: HTTP://XKCD.COM/329/
    6. 6. Social system theories andsimulationTop down Bottom up
    7. 7. From simulation toobservation (1/x) source:
    8. 8. From simulation toobservation (2/x)Focus groups, interviews,surveys Social media data Unspontaneous data  Spontaneously generated  Tradition of content analysis but: 1. Contents not anymore generated by an elite; 2. No filter before publishing.
    9. 9. From simulation toobservation (3/x)Simulation Observation Models  Real data
    10. 10. Conclusions (1/3) The theoretical effects of computational social science (CSS) on sociology and theory of social systems are not fully explored; CSS is criticized by some sociologists with the same arguments used against theories of social systems;
    11. 11. Conclusions (2/3) Can the display or the analysis of a conversations network, always detached from single individuals taking part to that conversation, help us fully understand the phenomenon under study? What is the relationship between the displays ofnetworks of communications and the relationship between individuals and society?
    12. 12. Conclusions (3/3) Most of the studies in CSS are developed by computer scientists (recall strong AI); Most of the concept developed in sociology is not enough well defined (recall weak AI); A stronger and more in depth collaboration is needed to fully express CSS potential.