A mathematical analysis of collective cognitive convergence

2019

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. A mathematical model of Collective Intelligence Federica Stefanelli, Enrico Imbimbo, Timoteo Carletti, Alessio Guarino, Francois Burette, Andrea Guazzini

2007

Abstract Cognitive agents for social environments are one of the most prevalent ideas in multi-agent design today. A vast majority of existing research either takes a centralist, static approach to organizational design or takes an emergent view which makes the behavior of the system as a whole is unpredictable. This paper elucidates our ideas on a general theory of collective behavior and structural formation. Our system is designed to achieve two goals.

Springer Proceedings in Complexity, 2013

We analyze the dynamics of agent-based models (ABMs) from a Markovian perspective and derive explicit statements about the possibility of linking a microscopic agent model to the dynamical processes of macroscopic observables that are useful for a precise understanding of the model dynamics. In this way the dynamics of collective variables may be studied, and a description of macro dynamics as emergent properties of micro dynamics, in particular during transient times, is possible.

2001

We distinguish between two main types of model: predictive and explanatory. It is argued (in the absence of models that predict on unseen data) that in order for a model to increase our understanding of the target system the model must credibly represent the structure of that system, including the relevant aspects of agent cognition. Merely “plugging in” an existing algorithm for the agent cognition will not help in such understanding.

… Computation (CEC), 2010 …, 2010

The self-organization of social networks and the emergence of ideas have both been studied extensively in recent years, but seldom in a single framework. In this paper, we describe a distributed multi-agent model for the selforganization of social networks from encounters between agents with specific ideas, which are seen as combinations of words. Each agent maintains a semantic network of the words it knows, which implicitly defines the ideas in its repertoire. Agents exchange their ideas over their social networks, and incorporate the received ideas in their semantic networks. Social bonds are made and broken based on the agents' social and semantic preferences (i.e., shared ideas), leading to the emergence of social communities. Thus, the model embodies a circular interaction between the formation of social networks and new ideas. We mine the resulting communities for novel ideas that are generated by their members, and look at the effect of interaction choices on their formation.

2020

Among all tools used to understand collective human behavior, few tools have been as successful as agent-based models (ABMs). These models have been particularly effective at describing emergent social behavior, such as spatial segregation in neighborhoods or opinion polarization on social networks. ABMs are particularly common in the study of opinion and belief dynamics, being used by fields ranging from anthropology to statistical physics. These models, much like the social systems they describe, often do not have unique output variables, scales, or clear order parameters. This lack of clearly measurable emergent behavior makes such complex ABMs difficult to study, ultimately limiting their application to cases of empirical interest. In this paper, we introduce a series of approaches to analyze complex multidimensional ABMs, drawing from information theory and cluster analysis. We use these approaches to explore a multi-level agent-based model of ideological alignment introduced b...

Cognitive Systems Research, 2001

In this paper, the problem of social order posed by infosocieties is illustrated and solutions are discussed. Two main solutions are currently implemented in the fields of Artificial Societies and Multiagent Systems, i.e. Evolutionary Algorithms, on one hand, and Designed Institutions, on the other. Both proceed from controversial assumptions. Evolutionary Algorithms are based upon the idea that reproduction and imitation of fit individuals yield social evolution. However, this idea has been efficaciously [Journal of Artificial Societies and Social Simulation 1 (1998) 3] questioned. The complementary solution, i.e. Designed Institutions, is based upon systems of incentives and sanctions. In this paper, these are argued to be unreliable and even counterproductive, especially when natural agents are involved. Emergent social Institutions and distributed control are thus discussed as an alternate solution to the problem of social order. Hypotheses about the co-evolutionary process, including mental and social structures, leading to Emergent Institutions are discussed, and advantages over the preceding solutions are illustrated. Unlike Evolutionary Algorithms, Emergent Institutions are based upon (implicit) social norms and reputation. Unlike Designed Institutions, Emergent Institutions give rise to spontaneous social control, which is more efficient and less costly than centralised control. Finally, the role of Emergent Institutions in infosocieties is examined.

Computing in Economics and Finance 2006, 2006

The present paper is dedicated to discussing a formal definition of emergence and related implications for Agent-based Computational Economics (ACE) models, through an example based on an extension of the ACE population game model of Axtell et al. (2001). Our definition deals with problem of modelling cognitive agents in complex artificial societies designed by Agent Oriented Programming. The extended discussion of our definition is discussed in a companion paper by Dessalles, Phan (2005). The present paper is an attempt to integrate both downward and upward causation in one single framework, in which the 'whole' is the result of the collective interactions between agents (upward causation / methodological individualism), but, in which the agents are in are to some extent constrained by the whole (downward causation), by way of the "social dimension" of their belief as well as their perception of social phenomena, phenomenon called "imergence" by Gilbert (1995).

2000

This paper describes a formal framework for the representation of agents that do not necessarily share common beliefs and goals. In this framework, that we consider to be a plausible model of cognitive agent behaviour in a multiagent system, the agents are able to reason based on assumptions while facing inconsistencies, manifest preferences over their beliefs, select and dynamically modify their attitude towards the beliefs of other agents. We use the same framework to support agent communication and rational reactions to communicative acts, and to develop plausible models of agents, without access to the internal structure of those agents. The main idea of this work is that default reasoning enhanced with preferences, as formerly proposed by the first author for the monoagent case, may uniformly support plausible solutions for the design and modeling of many agent features in open multiagent systems 1 .