本文主要探討存戶決策對銀行投資組合決策之影響，及其可能引發之系統性風險的程度。以代表性銀行開始分析，假設銀行投資於安全性資產與風險性資產，而存戶依其決策可能會產生兩個均衡同時存在之情形。本文利用演化的力量，發展出以風險性資產報酬率作為均衡選擇之標準：當風險性資產報酬率低於某一水準時，擠兌的均衡會單獨發生。並比較銀行考慮擠兌發生可能性時，其投資組合承擔風險之程度，及可能引發的銀行倒閉機率大小。推廣至二家模型，發現愈多家銀行在作投資決策考慮存戶擠兌之可能性，愈會增加經濟體系中之系統性風險，顯示銀行與存戶之衝突與金融體系之脆弱性。最後討論資本適足性管制與央行最後貸款人角色能否有效降低銀行倒閉機率。

在複雜動態競爭市場中，生產者的價格競爭行為一直是一個研究的重點，相較於生產者動態價格競爭，零售商的價格競爭行為鮮少被探討，因此本研究針對零售商價格競爭行為進行研究。針對零售商之間的價格競爭行為，除了考量零售商與對手零售商的價格互動，不可忽略的是上游供應商的競爭互動與下游消費者的學習行為在市場中與零售商端互動下錯綜複雜的動態影響，緣此，本研究以零售商端的角度，想了解供應商競爭與消費者學習行為對零售商競爭的影響，再以單一零售商角度，分析各情況下所應對的價格調整策略。 本研究將零售商、供應商及消費者互動形成之競爭市場視為一個複雜適應性系統（Complex Adaptive System ，簡稱CAS），應用代理人基塑模與模擬（Agent-based Modeling and Simulation，簡稱ABMS）方式建構考量供應商競爭與消費者學習行為之零售商價格競爭模式，將演化賽局理論應用於價格競爭中，探討不同的消費者學習及供應商價格競爭行為如何動態影響零售商價格競爭型態，以及不同價格調整策略之績效表現。 研究結果發現一，市場中消費者呈現不同的學習行為，對零售商競爭將造成不同的衝擊。「貨比三家無學習」型消費者將造成零售商端低價競爭，使其平均價格最低及獲利最低。「自我式學習」型消費者將造成零售商高價合作，使其平均價格最高及獲利最高。「群體式學習」型消費者同樣使零售商端偏向高價合作，且其平均價格及獲利相當接近自我式學習市場，雖然兩種學習行為具有近似的平均價格與獲利，「群體式學習」卻會導致零售商價格競爭之型態轉為劇烈，包括獲利領先轉換方式由漸進轉為瀑布，領先方式從勢均力敵轉為大幅領先，領先互換的頻率由低轉為高。另外，消費者購買決策之理性程度對零售商端競爭形態有影響，不論在何種供應商行為下，高理性購買決策在群體式學習下將導致零售商端價格競爭較激烈，在自我式學習下卻導致零售商端競爭行為較緩和。 研究發現二，市場中供應商的價格競爭行為會對零售商端的價格、獲利與競爭型態造成衝擊。供應商呈現價格競爭行為下，在「貨比三家無學習」之消費者行為市場中，將減緩零售商價格競爭，使零售商端之平均價格及獲利提高。在「自我」與「群體式」學習消費者市場中，將增強零售商價格競爭強度，使零售商端之平均價格及獲利降低。 研究發現三，不同的競爭市場中，零售商之最佳價格調整策略也將不同。基本上在供應商無競爭行為下，無論消費者呈現何種行為，零售商採取開放式價格調整策略具有明顯優勢。在供應商呈現競爭行為下，開放式價格調整策略在「無學習」及「群體式學習高理性程度」行為市場仍為優勝策略，在「自我式學習」及「群體式學習低理性程度」下，保守型價格調整策略則表現較佳。 在實務意涵上，若零售商可使消費者行為偏向自我或群體式學習，並穩定供應商價格競爭下，整體而言零售商端競爭可獲得最高的獲利，若當此刻競爭零售商採取保守型價格策略，而本身採取開放式價格調整策略，則獲利最大。然而面臨群體式學習消費者，由於競爭強度的增加，需留意市場動態，須隨時靈活調整本身價格策略，避免因價格策略的僵化，而成為虧損之零售商。 / The pricing competitive model traditionally assumes that consumers will buy from the firm selling the homogeneous product at the lowest price, thus discarding any possibility of learning behavior on the demand side. But if, as in real competition, consumers learn adaptively and competition is a dynamic process, then some attention should be paid to consumers' behavior. In a multiple supplier – multiple retailer supply chain, multiple price competitive forces interact to influence firm price decisions. These forces include: (1) the supplier level competition each supplier faces from others producing the same product, (2) the retailer level competition among the retailers selling the same set of goods, and (3) the vertical interaction competition between the retailer and supplier. We are interest in these questions: How does the consumer learning behavior affect the retailer pricing competitive model? How does the competition of supplier affect the retailer pricing competitive model? What is the optimal adaptive pricing strategy for retailer performance in such competitive market including retailers, suppliers and consumers. Therefore, this research study a version of the pricing competitive (Bertrand) model in which consumer exhibit dynamic adaptive learning behavior when deciding from what retailers they will buy. And we consider to join the supplier competitive pricing behavior into the retailer pricing competitive model and formulate their interaction as evolutional game and to analyze the competition of supplier effect and its impact on the pricing competition of retailers. This research uses a complex adaptive system perspective to construct a retailer pricing competitive model which considers both competitive supplier and learning consumer behavior. Using agent-based modeling and simulation (ABMS) to construct the competitive market include retailers, suppliers and consumers, and use the fuzzy logic, genetic algorithms to model the pricing decision and learning behavior of retailers and suppliers, and use reinforcement learning and swarm algorithms to model consumers’ learning behavior. The simulation results demonstrate that: The retailer level obtains the highest profit when the consumer behavior following reinforcement learning. When the consumer behavior displays swarm learning, the retailer level also obtains high profit near the highest profit. However swarm learning increases the competitive intensity on the retailer level. The competitive supplier increases the competitive intensity and decrease profit on the retailer level when the consumer behavior displays reinforcement learning and swarm learning. The performance of retailer following a closed adaptive pricing strategy (high exploitation low exploration) exceeds that of retailer following an open adaptive pricing strategy (low exploitation high exploration) when the consumer behavior displays reinforcement learning and supplier display competitive behavior. However when the consumer behavior displays swarm learning and supplier display competitive behavior, the performance of retailer following an open adaptive pricing strategy exceeds that of retailer following a closed adaptive pricing strategy. The proposed pricing competitive model with adaptive learning of consumer behavior and competition of supplier can help retailers to analyze pricing strategy and further discovery and design the more optimal pricing strategy.

本論文主要是在agent-based計算經濟體系下，利用Holland(1975)所提的遺傳演算法(genetic algorithms)作為計算工具，分別探討連鎖店賽局及寡占市場廠商價格策略的生態演化。 在連鎖店賽局的研究中，藉由agent-based計算經濟分析掠奪性定價的特性，並進一步探討參賽者價格策略的共演化(co-evolutionary)特性，及多元均衡賽局中均衡移轉的動態過程。針對賽局中不同的不確定性進行模擬，結果顯示廠商長期總合行為是否穩定，和賽局中的不確定程度有相當的關聯。另外，弱獨占者和潛在競爭者的價格策略存在著共演化特性。在此演化賽局中，Nash均衡雖非穩定均衡解，但卻最常浮現在長期總合行為中。因此，Nsah均衡對agent-based演化賽局的結果而言，相當具有參考價值。在特定的不確定程度下，長期總合行為似乎只在某些特定的Nash均衡中徘徊。這些移轉途徑並不具有對稱性，甚至移轉速度也非對稱。本研究所呈現的演化結果跳脫一般對均衡的觀念，展現出傳統理論所無法預知的共演化特性，並呈現出非對稱的吸引環。 此外，同樣在Agent-based計算經濟下探討寡占市場廠商策略生態。本研究首先闡明N參賽者囚犯兩難重複賽局和N廠商寡占賽局之間的異同，經由寡占賽局廠商償付矩陣(payoff matrix)的狀態相依馬可夫移轉矩陣( state-dependent Markov transition matrix)性質，說明N廠商寡占賽局和N參賽者囚犯兩難重複賽局的差異。其次，透過三家廠商寡占賽局的模擬實驗，以遺傳演算法建構參賽廠商的適應性行為，分別以寡占市場生態上的表現型(phenotypes)和基因型(genotype)進行分析。20次模擬結果所呈現的最終市場狀態相當分歧，有形成吸引環的三廠商寡占市場、有收斂到價格戰的三廠商寡占市場。另外也成功的模擬出三廠商寡占市場演化至雙佔市場、甚或獨占市場的過程。但是，在眾多模擬中並沒有發現持續的勾結定價狀態，反而掠奪性價格是較主要的價格策略。這些結果相對於實際經濟社會中的寡占市場，給予一個活潑生動的範例。 / Recently, genetic algorithms have been extensively applied to modeling evolution game in agent-based computational economic.　While these applications advance our understanding of evolution game, they have generated some new phenomena that have not been well treated in conventional game theory. In the first topic, we shall systemize the study of one of these new phenomena, namely, coevolutionary instability. We exemplify the basic properties of coevolutionary instability by the chain store game, which is the game frequently used to study the role of reputation effects in economics.　In addition, we point out that, while, due to uncertainty effects, Nash equilibria can no longer be stable, and they can still help us predict the dynamic process of the game.　In particular, we can see that the dynamic process of the game is well captured by a few Nash equilibria and the transition among them.　A careful study can uncover several interesting patterns and we show the impact of uncertainty on these patterns. In the second topic, the relation between the N-person IPD game and the N-person oligopoly game is rigorously addressed.　Our analytical framework shows that due to the path-dependence of the payoff matrix of the oligopoly game, the two games in general are not close in spirit.　We then further explore the significance of the path-dependence property to the rich ecology of oligopoly from an evolutionary perspective.　More precisely, we simulated the evolution of a 3-person oligopoly game, and showed that the rich ecology of oligopoly can be exhibited by modeling the adaptive behavior of oligopolists with genetic algorithms.　The emergent behavior of oligopolists are presented and analyzed.　We indicate how the path-dependence nature may shed light on the phenotypes and genotypes coming into existence.