How could a rational analysis model explain?

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances

Exploring the N-th Dimension of Language

This paper is aimed at exploring the hidden fundamental\ud computational property of natural language that has been so elusive that it has made all attempts to characterize its real computational property ultimately fail. Earlier natural language was thought to be context-free. However, it was gradually realized that this does not hold much water given that a range of natural language phenomena have been found as being of non-context-free character that they have almost scuttled plans to brand natural language contextfree. So it has been suggested that natural language is mildly context-sensitive and to some extent context-free. In all, it seems that the issue over the exact computational property has not yet been solved. Against this background it will be proposed that this exact computational property of natural language is perhaps the N-th dimension of language, if what we mean by dimension is\ud nothing but universal (computational) property of natural language

The Current State of Normative Agent-Based Systems

Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling