Multi-agent simulation and unemployment

Agent-based simulation reports a project in agents-based computer simulation of processes of economics in a population of boundedly rational learning agents. The same family of economics models will be simulated under different assumptions about the nature of the learning process and details of the production and employment process. The purpose of this procedure is to establish a relationship between the assumptions and the simulation results. The simulation techniques with baseline simulations of boundedly rational learning processes that do not involve the complications of dealing with long-run economic growth. Can simulation explain', the puzzles of economic growth and particularly the key puzzle of growth and learning processes that produce the puzzling results? And just What assumptions oh the simulation arc riot predictable associated with puzzling results'? We have modeled more and more as interactions between agent and the environment or between different agents the emergence of beliefs as well as the specific type of informational treatment traditionally called.,rationality. Modeling belief's has became a specific field of decision making. particularly respect to probability events. Rational decision making leas also been generalized in order to encompass. in new Nays and in more general situations that it used to be fitted to. multiple dimension ill consequences. The difficult question to incorporate several of these recent advances simultaneously and it offers trends of modeling such complex economics systems and deals will) extensions of the economic theory. Specific elements in the development of interactions between agents and their environment and confronts real-world problem in both financial and non-financial and decisions. and tries to show what kind of contribution call be brought to them by the type of modeling reported oil here, Many agent-based systems rely for their effectiveness oil the individual agents. among interaction agents is required to coordinate these individually complex decision. All agent may take tin effort a set of efforts and the output will depends on the random state of nature. The output will rise stochastically as the level of forts increases and (lie state of nature improves additional information is valuable because it allow's a more accurate judgement of performance of the agent, Typical problems is mufti-agents case call also offer opportunities for inference of hidden action and agent monitoring agent. Multi agents require a hierarchical organization to coordinate tasks and monitoring agents' input and information costs can be reduced and productivity improves.