Talking Nets : A Multi-Agent Connectionist Approach to Communication and Trust between Individuals

A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions. TALKING NETS Model of Communication 2 Cognition is not limited to the mind of an individual agent, but involves interactions with other minds. A full understanding of human thinking thus requires insight into its social development. Sociologists and developmental psychologists have long noted that most of our knowledge of reality is the result of communication and social construction rather than of individual observation. Moreover, important real-world problems are often solved by a group of communicating individuals who pool their individual expertise while forming a collective decision that is supposedly better than what they might have achieved individually. This phenomenon may be labeled collective intelligence (Levy, 1997; Heylighen, 1999) or distributed cognition (Hutchins, 1995). To understand such group-level information processing, we must consider the distributed organization constituted by different individuals with different forms of knowledge and experience together with the social network that connects them and that determines which information is exchanged with whom. In addition to qualitative observations and conceptualizations, the phenomenon of distributed cognition has been studied in a quantitative, operational manner, although from two very different traditions: social psychology and multi-agent simulations. Social psychologists have typically studied group level cognition using laboratory experiments, and their research has documented various fundamental shortcomings of collective intelligence. We often fall prey to biases and simplistic stereotypes about groups, and many of these distortions are emergent properties of the cognitive dynamics in interacting minds. Examples are conformity and polarization which move a group as a whole towards more extreme opinions (Ebbesen & Bowers, 1974; Mackie & Cooper, 1984; Isenberg, 1986), communication within groups which reinforce stereotypes (e.g., Lyons & Kashima, 2003), and the lack of sharing of unique information so that intellectual resources of a group are underused (Larson et al., 1996, 1998; Stasser, 1999; Wittenbaum & Bowman, 2004). Although this research provides empirical evidence about real people to convincingly test particular hypotheses, it is often limited to small groups with minimal structures performing tightly controlled tasks of limited duration. In contrast, the approach of multi-agent systems originates in distributed artificial intelligence (Weiss, 1999) and the modeling of complex, adaptive systems, such as animal swarms (Bonabeau, Dorigo & Theraulaz, 1999) and human societies (Epstein & Axtell, 1996; Nowak, Szamrej & Latané, 1990). In these systems, agents interact in order to reach their individual or group objectives, which may be conflicting. However, the combination of their local, individual actions produces emergent behavior at the collective level. In contrast to experimental psychology, this approach has been used to investigate complex situations, such as the emergence of cooperation and culture, and the self-organization of the different norms and institutions that govern the interactions between individuals in an economy. It manages to tackle such complex problems by means of computer simulations in which an unlimited number of software agents interact according to whatever simple or complex protocols the researcher has programmed them to obey. Although the complexity of situations that can be studied is much greater than in an experimental paradigm, there is no guarantee that the results produced by the simulation say anything meaningful about real human interaction. TALKING NETS Model of Communication 3 Moreover, many of the earlier simulations to represent agent interaction are too rigid and simplistic to be psychologically plausible. The behavior of a typical software agent is strictly rule-based: If a particular condition appears in the agent's environment, the agent will respond with a particular, preprogrammed action. More sophisticated cognitive architectures may allow agents to learn, that is, adapt their responses to previous experience, but the learning will typically remain within a symbolic, rule-based level. Perhaps the most crucial limitation of many models is that the individual agents lack their own psychological interpretation and representation of the environment. As Sun (2001, p. 6.) deplored, multi-agent systems need “. better understanding and better models of individual cognition”. Another shortcoming of many multi-agent models is their rigid representation of relations between agents. In the most common models, cellular automata, each agent (“automaton”) occupies a cell within a geometrical array of cells, typically in the form of a checkerboard. Agents will then interact with all the agents in their geometric neighborhood. This is clearly a very unrealistic representation of the social world, in which individuals interact differentially with other individuals depending on their previous experiences with those others. Other types of simulations, such as social network models, are more realistic with respect to human relationships, but still tend to be rigid in the sense that the agents cannot change the strength of their relationship with a particular other agent (for a full discussion, see section on Alternative Models). The present paper presents a first step towards overcoming these various limitations, by proposing an integration of social psychology and multi-agent approaches. For this, we will present a generic multi-agent simulation environment that is as much as possible psychologically plausible, while at the same time remaining as simple as possible. First, each individual agent is represented by a recurrent connectionist network (to be described shortly) that is capable of representing internal beliefs as well as external information. Such a network can learn new associations between its different concepts through experience. This type of model has been used successfully in the past to model several phenomena in social cognition, including person impression formation (Smith & DeCoster, 1998; Van Overwalle & Labiouse, 2004), group impression formation (Kashima, Woolcock & Kashima, 2000; Queller & Smith, 2002; Van Rooy, Van Overwalle, Vanhoomissen, Labiouse & French, 2003), attitude formation (Van Overwalle & Siebler, 2005), causal attribution (Van Overwalle, 1998; Read & Montoya, 1999), and many other social judgments (for a review, see Read & Miller, 1998). Second, and this is the real innovation proposed in this paper, we extend this connectionist model of individual cognition to the social relations between agents. Inspired by previous models developed by Hutchins (1991) and Hutchins and Hazlehurst (1995), our model consists of a collection of individuals networks, that each represent a single individual, and that can communicate with each other. This is depicted in Figure 1 for three agents. In our model, unlike earlier multi-agent models, any agent can in principle interact with any other agent, but the strength of the interaction will adapt to experience. We will introduce the fundamental concept of cognitive trust as a measure of the degree to which one agent influences another. Cognitive trust can be defined as the confidence that one agent has in the information communicated by another one. It thus refers to the perceived TALKING NETS Model of Communication 4 validity or credibility of received information given the receiver’s knowledge about the sender. Note that this is different from the more common meaning of trust which —apart from other specific uses in law and business— can be broadly summarized as the expectation that another agent would perform some action (as part of an exchange, as a result of one’s relationship with the other, or of the power one has over the other). In the remainder of the paper, however, we will use the shorthand term trust to refer to cognitive trust. Communication involves the transmission of information on the same concepts from one agent’s network to another agent’s network, along connections whose adaptive weights reflect the perceived trust in the information transmitted. Hence, the group of agents functions as an adaptive, socially distributed network where information and knowledge are distributed among and propagated between different individual networks. To demonstrate that this model is realistic, we will run several simulations that can accurately reproduce several of the key experimental results obtained by psychologist