By K. Arai, H. Deguchi, H. Matsui
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Additional resources for Agent-Based Modeling Meets Gaming Simulation
C. Disparity of committee activities: this indicator shows the difference of the number of active terms between the most active and least active committees. It shows the efﬁciency of the designed system and the number of citizen complaints. Garbage Can Model for Design of Citizen Participation System 45 Fig. 4. Relationships among the committees In the simulation cases, it is assumed that the problem arrival ratio is different from the citizen attendance percentage and 20 simulations are allowed for each case.
Demand forecast after two rounds is shown to a player. Number of software agents: six agents with Exploring Business Gaming Strategies by Learning Agents 53 a. Reactive strategy (three agents), b. Hand-coded rules of a human player (two agents), and c. 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.
A reinforcement learning system with proﬁt sharing as a decision-making tool is used for an agent who makes a decision depending on rules in the classiﬁer system. The action rule parameters in the reinforcement learning system are the ﬁrms’ cash, and the difference between their technological levels and average technological level. Firm agents select and carry out an action rule from their action rule sets in proportion to the weight of the action rule (Roulette selection). The set of action rules is shown in Eq.
Agent-Based Modeling Meets Gaming Simulation by K. Arai, H. Deguchi, H. Matsui