Functional Model of Criminality: Simulation Study

Abstract

Abstract. We describe a new point of view that uses contextual information of the interrelated conditions in which criminality or deviant occurs by means of computer simulation to examine its role in a progressively changing society. Based on the functionalist point of view, we model society as a multi agent system and criminality as a noise. A noise is injected as training signals to the single layer perceptrons that functions as agents (i.e. society) and all agents must comply the fitness function. Failure to comply will result in the agent being removed from the "society" and be replaced by a "newborn" that inherits some "upbringing" information from its "mother's agent". Our simulation studies point toward the constructive effects of criminality. The major contribution in our study is that new paradigm can show and measure criminality effects as in contrast to mere verbal descriptions of the social sciences. The new paradigm could be used to provide the functional explanation of atypical trends in criminality in East-and Middle European and some other countries and provide "artificial experimental data" for future studies.

Effect of Synthetic Emotions on Agents’ Learning Speed and Their Survivability

“…Obviously, these factors could be included into the emotions model to make it more suitable to analyze real world problems. Useful extension of the model is analysis of learning process utilized to solve not a single, but a variety of varying pattern recognition tasks, like it was done in a recent research papers on aging [25] and criminality [26]. In analysis of populations of intelligent agents, one can consider situations where successful members of the populations can produce offspring, a strategy widely widespread in Nature [26,27].…”

“…Useful extension of the model is analysis of learning process utilized to solve not a single, but a variety of varying pattern recognition tasks, like it was done in a recent research papers on aging [25] and criminality [26]. In analysis of populations of intelligent agents, one can consider situations where successful members of the populations can produce offspring, a strategy widely widespread in Nature [26,27]. Including of the factors just mentioned, organizing of the populations into sub-groups with mechanisms of self-support of the agents inside the sub-group and restricted beneficial cooperation between them, and incorporation of the mechanisms of synthetic emotions into learning rule is a topic for future research in order to crate more realistic multi agent systems capable to survive in permanently changing environments.…”