Improving Learning in Business Simulations with an Agent-Based Approach

摘要: Artificial society simulations may provide unprecedented insight into the intricate dynamics of economic markets. Such an help solve well-known black-box dilemma business simulations, where designers prefer model concealment over transparency. The core contribution this work is agent-based simulation that models marketplace as artificial consumers. In simulation, users assume role a store owner playing against intelligence competitor. The can be accessed via graphical user interface animates decision behavior Consumers are modeled agents with concrete beliefs, intentions and desires act to maximize their utility accomplish purchase plans. We claim unlike classical equation-based approach, visualization market facilitated by our approach important information user. We hypothesize such key understanding several concepts. To validate hypothesis, we conducted experiment 30 users, compared effects animation market. Our results indicate has better learning outcomes both at level users' subjective self-assessment objective performance metrics knowledge acquisition tests. As secondary contribution, demonstrate example how simple codification rules functions allow emergence diverse macroeconomic two-product duopoly.