Computational Modeling of Culture's Consequences

This paper presents an approach to formalize the influence of culture on the decision functions of agents in social simulations. The key components are (a) a definition of the domain of study in the form of a decision model, (b) knowledge acquisition based on a dimensional theory of culture, resulting in expert validated computational models of the influence of single dimensions, and (c) a technique for integrating the knowledge about individual dimensions. The approach is developed in a line of research that studies the influence of culture on trade processes. Trade is an excellent subject for this study of culture’s consequences because it is ubiquitous, relevant both socially and economically, and often increasingly cross-cultural in a globalized world

Human Nature in the Adaptation of Trust

This chapter pleads for more inspiration from human nature, in agent-based modeling.As an illustration of an effort in that direction, it summarizes and discusses an agentbased model of the build-up and adaptation of trust between multiple producers and suppliers.The central question is whether, and under what conditions, trust and loyalty are viable in markets.While the model incorporates some well known behavioural phenomena from the trust literature, more extended modeling of human nature is called for.The chapter explores a line of further research on the basis of notions of mental framing and frame switching on the basis of relational signaling, derived from social psychology.trust;transaction costs;buyer-supplier relationships;social psychology

Adaptive build-up and breakdown of trust: An agent based computational approach

This article employs Agent-Based Computational Economics (ACE) to investigate whether, and under what conditions, trust is viable in markets. The emergence and breakdown of trust is modeled in a context of multiple buyers and suppliers. Agents develop trust in a partner as a function of observed loyalty. They select partners on the basis of their trust in the partner and potential profit, with adaptive weights. On the basis of realized profits, they adapt the weight they attach to trust relative to profitability, and their own trustworthiness, modeled as a threshold of defection. Trust and loyalty turn out to be viable under fairly general conditions.Agent-based computational economics;Inter-firm relations;Transaction costs;Governance;Trust;Complex adaptive systems

Human Nature in the Adaptation of Trust

This chapter pleads for more inspiration from human nature, in agent-based modeling.As an illustration of an effort in that direction, it summarizes and discusses an agentbased model of the build-up and adaptation of trust between multiple producers and suppliers.The central question is whether, and under what conditions, trust and loyalty are viable in markets.While the model incorporates some well known behavioural phenomena from the trust literature, more extended modeling of human nature is called for.The chapter explores a line of further research on the basis of notions of mental framing and frame switching on the basis of relational signaling, derived from social psychology.

Feasibility of Multi-agent Simulation for the Trust and Tracing Game

Trust is an important issue in trade. For instance in food trade, market actors have to rely on their trade partner's quality statements. The roles of trust and deception in supply networks in various cultural and organisational settings are subject of research in social sciences. The Trust And Tracing game is an instrument for that type of study. It is a game for human players. Conducting experiments is time-consuming and expensive. Furthermore, it is hard to formulate hypotheses and to test effects of parameter changes, as this requires many participants. For these reasons the project reported in this paper investigated the feasibility of multi-agent simulation of the game and deliverd a prototype. This paper briefly describes the game and introduces the process composition of the agents. The prototype uses simple, but effective models. The paper concludes wit directions for refinement of models for agent behaviou