Download Agent-Based Modeling Meets Gaming Simulation (Agent-Based by Kiyoshi Arai, Hiroshi Deguchi, Hiroyuki Matsui PDF

By Kiyoshi Arai, Hiroshi Deguchi, Hiroyuki Matsui

This choice of first-class papers cultivates a brand new point of view on agent-based social procedure sciences, gaming simulation, and their hybridization. lots of the papers integrated the following have been offered within the distinctive consultation titled Agent-Based Modeling Meets Gaming Simulation at ISAGA2003, the thirty fourth annual convention of the overseas Simulation and Gaming organization (ISAGA) at Kazusa Akademia Park in Kisarazu, Chiba, Japan, August 25–29, 2003.

Nowadays, agent-based simulation is turning into extremely popular for modeling and fixing advanced social phenomena. it's also used to reach at useful recommendations to social difficulties. even as, even if, the validity of simulation doesn't exist within the beauty of the version. R. Axelrod stresses the simplicity of the agent-based simulation version in the course of the “Keep it uncomplicated, stupid” (KISS) precept: As a fantastic, easy modeling is essential.

Many genuine social phenomena are extra advanced than should be defined through the straightforward modeling precept, despite the fact that. we have to build a version of advanced phenomena as a reproduction of a true scenario. it really is tough to mix fact and sim- plicity in a version, specifically whilst the phenomena comprise many brokers as selection makers. How will we make the 2 various modeling ideas compatible?

Gaming and simulation deals a solution. If a human participant can perform a simulation version as a player–agent, then he can simply realize the truth of the version. Hybrid simulation makes attainable the hybridization of gaming simulation and agent-based simulation in a version. In a hybrid simulation, version human gamers and computer brokers simulate (play) the version while.

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Extra resources for Agent-Based Modeling Meets Gaming Simulation (Agent-Based Social Systems, Volume 2)

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XCS-based machine learning agents (one agent). Using the game, we design the following experiments with three phases: Phase 1: gaming experiments with only human players to study the basic performance of the game and players; Phase 2: gaming with software players with simple hand-coded decision rules and human players to validate the feasibility of the software agent players; Phase 3: gaming with software agents with hand-coded rules and a learning agent with XCS. We have implemented four kinds of software agents as alternatives to human players for Phase 2.

Most of the citizens who are involved in these activities are motivated to volunteer out of a sense of civic duty, but naturally they are restricted by their work and private life. Sometimes they are absent from meetings and, although nobody feels there is a conflict, the situation is not ideal for the municipal officers responsible for executing projects. This is a classic example of “organized anarchy” as described by March [1], which sometimes means that despite the citizens’ best efforts results are not achieved efficiently.

Econophysical analysis did not confirm the random walk assumption in price fluctuation obtained from the experiment, and a farflung distribution called “high peak, fat tail” was observed. Especially interesting is the emergence of potent traders from ranks of complete novices in actual securities trading, let alone futures trading, although it was in the limited market. They took advantage of level data, enjoyed high execution rates, and constantly achieved high realized profit. They are so-called stockjobbers.

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