13,836 research outputs found

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

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    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

    Facing Openness with Socio Cognitive Trust and Categories.

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    Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experiences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed membership to categories; (2) learn a series of emergent relations between trustees observable properties and their effective abilities to fulfill tasks in situated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of architectures combining categorization abilities, individual experiences and context awareness are evaluated and compared in simulated experiments

    Agent-oriented Modeling for Collaborative Learning Environments: A Peer-to-Peer Helpdesk Case Study

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    In this paper, we present the analysis and modelling of Help&Learn, an agent-based peer-to-peer helpdesk system to support extra-class interactions among students and teachers. Help&Learn expands the student’s possibility of solving problems, getting involved in a cooperative learning experience that transcends the limits of classrooms. To model Help&Learn, we have used Agent-Object-Relationship Modeling Language (AORML), an UML extension for agent-oriented information systems modeling. The aim of this research is two-fold. On one hand, we aim at modeling the variety of roles and the complexity of their interactions and activities within the Help&Learn system. On the other hand, we aim at showing the expressive power and the modeling strengths of AORML

    Collective dynamics of belief evolution under cognitive coherence and social conformity

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    Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework explains how social instabilities can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We therefore argue that the inclusion of cognitive factors into a social model is crucial in providing a more complete picture of collective human dynamics

    Modeling cultural change: computational models of interpersonal influence dynamics can yield new insights about how cultures change, which cultures change more rapidly than others, and why

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    Cultural change can occur as an emergent consequence of social influence dynamics within cultural populations. These influence dynamics are complex, and formal modeling methods-such as agent-based models-are a useful means of predicting implications for cultural change. These models may be especially useful if they not only model the psychological outcomes of interpersonal influence, but also model social network structures within a culture. When combined, these components provide a flexible modeling framework that allows other variables to also be modeled for the purposes of predicting plausible implications for cultural change. The article illustrates this approach by summarizing recent research that used these methods to model cross-cultural differences in the pace of cultural change. The article then identifies additional variables that could potentially be modeled within this conceptual framework, to produce additional insights-and additional new hypotheses-about different circumstances associated with different patterns of cultural change

    DYNASTAT: A Methodology for Dynamic and Static Modeling of Multi-agent Systems

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    Multi-agent systems are increasingly being used within various knowledge domains. The need for modeling of the multi-agent systems in a systematic and effective way is becoming more evident. In this chapter, we present the DYNASTAT methodology. This methodology involves a conceptual overview of multi-agent systems, a selection of specific agent characteristics to model, and a discussion of what has to be modeled for each of these agent characteristics. DYNASTAT is independent of any particular modeling language but provides a framework that can be used to realize a particular language in the context of a real-world example. UML 2.2 was chosen as the modeling language to implement the DYNASTAT methodology and this was illustrated using examples from the medical domain. Several UML 2.2 diagrams were selected including a use case, composite structure, sequence and activity diagram to model a multi-agent system able to assist botha medical researcher and a primary care physician. UML 2.2 provides a framework for effective modeling of agent-based systems in a standardized way which this chapter endeavors to demonstrate

    A connectionist ABM of social categorization processes

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    This paper introduces a connectionist Agent-Based Model (cABM) that incorporates detailed, micro-level understanding of social influence processes derived from laboratory studies and that aims to contextualize these processes in such a way that it becomes possible to model multidirectional, dynamic influences in extended social networks. At the micro-level, agent processes are simulated by recurrent auto-associative networks, an architecture that has a proven ability to simulate a variety of individual psychological and memory processes [D. Van Rooy, F. Van Overwalle, T. Vanhoomissen, C. Labiouse and R. French, Psychol. Rev. 110, 536 (2003)]. At the macro-level, these individual networks are combined into a “community of networks” so that they can exchange their individual information with each other by transmitting information on the same concepts from one net to another. This essentially creates a network structure that reflects a social system in which (a collection of) nodes represent individual agents and the links between agents the mutual social influences that connect them [B. Hazlehurst, and E. Hutchins, Lang. Cogn. Process. 13, 373 (1998)]. The network structure itself is dynamic and shaped by the interactions between the individual agents through simple processes of social adaptation. Through simulations, the cABM generates a number of novel predictions that broadly address three main issues: (1) the consequences of the interaction between multiple sources and targets of social influence (2) the dynamic development of social influence over time and (3) collective and individual opinion trajectories over time. Some of the predictions regarding individual level processes have been tested and confirmed in laboratory experiments. In a extensive research program, data is currently being collected from real groups that will allow validating the predictions of cABM regarding aggregate outcomes
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