Evolutionary Microeconomics and the Theory of Expectations

This paper sketches a framework for the analysis of expectations in an evolutionary microeconomics. The core proposition is that expectations form a network structure, and that the geometry of that network will provide a suitable guide as to the dynamical behaviour of that network. It is a development towards a theory of the computational processes that construct the data set of expectations. The role of probability theory is examined in this context. Two key issues will be explored: (1) on the nature and stability of expectations when they form as a complex network; and (2), the way in which this may be modelled within a multi-agent simulation platform. It is argued that multi-agent simulation (a-life) techniques provide an expedient analytical environment to study the dynamic nature of mass expectations, as generated or produced objects, in a way that bridges micro and macroeconomics.

An Agent-Based Collaborative Approach to Graphing Causal Maps for Situation Formulation

We provide a background discussion of group support systems (GSS) research into aiding strategic management processes. GSS support for strategic management has been primarily focused on qualitative analysis and the communication processes surrounding strategic planning. While fully developed in common decision-support systems, powerful simulation modeling and quantitative analytical tools have been difficult to integrate into GSS system configurations because they require increased cognitive load and expert modeling support, a central problem now addressed by collaboration engineering. A conceptual and functional bridge is needed to integrate the qualitative and quantitative approaches, reduce cognitive load, and provide modeling support that does not require experts. Acar’s analytical causal mapping is introduced as a structured method for situational formulation and analysis of unstructured strategic problems. This form of causal mapping includes specific processes and analytical approaches offering cognitive modeling support for problem formulation. Its computational capabilities provide support for Systems Thinking approaches in a system easy to learn and use. Using the methodological template of the design science paradigm, we contribute a prototype system for the development and simulation of causal maps that uses RePast 2.0, a Java agent-based modeling (ABM) and simulation library

An architecture for rational agents interacting with complex environments

In this paper we sketch an agent architecture suitable to be used as a tool for exploring agent perception and multiagent interaction. Nowadays, there is no strict correspondence between the theoretical work in rational agents and their implementation. In this respect, it is our intention to reach a good trade-off between expressiveness and implementability.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI