軟體元件電子市集突現：以代理人為基礎之計算經濟研究途徑 摘 要 軟體發展與演進過程中，產生軟體危機問題，而軟體再用是解決軟體危機的重要因應之道。軟體元件電子市集的興起是軟體演進史上一個重要里程碑，提供軟體再用的核心基礎建設。 本文探討軟體元件電子市集突現的本質原因和信任關係的發展過程，以遺傳規劃法(Genetic Programming, GP)為主的代理人基礎的計算經濟 (Agent-based Computational Economics, ACE) 研究途徑，整合軟體元件特性、交易成本、滿意和信任關係建立模擬模式。藉以觀察和分析底層買賣雙方連續滿意交易與信任關係發展，和上層軟體元件電子市集行為突現(emerge)動態過程。 結果顯示：在市場力量下，具標準化軟體元件，電子市集行為突現過程中，謹慎型交易策略將會勝出，進而主導整個市場。當元件功能特殊性程度低時，電子市集行為的購買率將比元件功能特殊性程度高者更為顯著。如果考慮交易態度滿意與否，記憶型滿意者市集行為的購買率將顯著低於高滿意型，而顯著高於低滿意型。若考慮不同信任程度函數，高信任型電子市集購買率顯著高於低信任型，低信任型其電子市集購買率顯著高於不信任型，對於目錄型市集行為和忠誠目錄型市集行為，上述信任函數的形態亦依序顯著影響購買率的高低。 同時，在不同信任型之間，高信任型大多數有連續累積交易行為；而低信任型則同時採用連續和臨時交易行為；不信任型大多數是臨時交易行為，要花費更多時間的演化，以建立彼此信任關係才會出現連續交易乃至於連續累積交易行為。 關鍵字：軟體元件電子市集、交易成本、遺傳規劃法、代理人基礎計算經濟、信任、突現 / The Emergence of Software Component Electronic Marketplaces: Through An Agent-based Computing Economics Approach Abstract Software reuse plays a vital role in response to software crises in software evolution. An emergence of software component e-marketplace is one of the great milestones providing a core infrastructure for software reuse. The objective of this study involving features of s/w components, transaction costs and satisfaction-trust relations intends to understand why s/w component e-marketplaces emerge as well as demonstrate how they do. The model allows agents to develop their trust in the market as a function of continuation of a satisfied relation through an agent-based computational economics approach with genetic programming. The findings show that the agents with prudent strategies tend to dominate the market in evolution of e-marketplaces under the market power. In addition, the lower level the functional particularity of component is, the higher the buying rate is. As the satisfaction attitude is taken into consideration, the buying rate of recall-satisfied agents lies between that of low-satisfied agents and that of high-satisfied agents. Moreover, when the comparisons are made among the three types of trust function, the buying rate of the high-trust agent is higher than that of low-trust agents. And the buying rate of the low-trust agent is bigger than that of not-trust agents. Similarly, the sequences of the buying rate are strongly influenced by different type of trust function at the catalog market and the loyal catalog market. Meanwhile, almost all high-trust agents have continuous and loyal trade behavior. Either continuous or temporal trade behavior is usually found in the low-trust agents. The tentative trade behavior is seen among almost every not-trust agents. In another words, it is well obvious that it takes more time for the not-trust agents to accumulate trust from their possible trade partners. Keywords: Software component electronic marketplaces; Transaction costs; Genetic programming (GP); Agent-based computational economics (ACE); Trust, Emergence