在代理人基計算建模(agent-based computational modeling)被拿來廣泛應用的同時，多數學者發現模擬的結果會高度取決於人工適應性個體的設計方式或者是個體的學習方法上，所以如何挑選合適的演算法就成為我們應用代理人基計算建模時首要面臨的課題。

本文挑選了兩個常出現於文獻當中但是卻甚少一起比較的演算法，分別是遺傳演算法(genetic algorithms)與強化學習(reinforcement learning)。我們透過將演算法與學習理論(learning theory)結合的方式，歸納出這兩個高使用頻率的演算法各自有其適合描述的個體行為以及議題，最後並套用到代理人基彩券市場當中，而模擬的結果也證實符合真實彩券市場上多數人學習特性(個人式學習)的強化學習比起遺傳演算法更能完整地捕捉彩券市場上的特性。

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