Complexity and Competition

Extensive‐form market games typically have a large number of noncompetitive equilibria. In this paper, we argue that the complexity of noncompetitive behavior provides a justification for competitive equilibrium in the sense that if rational agents have an aversion to complexity (at the margin), then maximizing behavior will result in simple behavioral rules and hence in a competitive outcome. For this purpose, we use a class of extensive‐form dynamic matching and bargaining games with a finite number of agents. In particular, we consider markets with heterogeneous buyers and sellers and deterministic, exogenous, sequential matching rules, although the results can be extended to other matching processes. If the complexity costs of implementing strategies enter players’ preferences lexicographically with the standard payoff, then every equilibrium strategy profile induces a competitive outcome.     

基于产业集群的企业重点投资地区决策的仿真

用多Agent仿真技术,研究了基于产业集群的台资IT企业在大陆重点投资地区的决策问题。提出了针对这一问题的、将集群竞争力评价的GEM模型、多Agent仿真技术及演化博弈相结合的思路与方法。仿真模型对集群的研究方法以及多Agent的应用领域有拓展意义,而仿真发现与结论可为政府制定有关政策提供参考。     

零售市场价格策略的演化博弈分析

基于一个三阶段Hotelling博弈模型:位于[0,1]线性城市两端的零售A和B第一阶段同时选择价格策略变量,第二阶段确定价格的大小,第三阶段由消费者选择零售商。应用演化博弈论进行研究,分析了零售市场价格策略的演化稳定性。     

智能体建模和资本市场复杂性

以复杂适应系统的思想和智能体建模的方法研究资本市场复杂性是新兴的有价值的研究领域,阐述了资本市场作为复杂适应系统的动力机制,介绍了这一领域一个重要模型———少数派博弈(minority game,MG)模型.仿真发现,处于拥挤阶段的MG具有和实际市场相近的收益率分布.进一步扩展标准MG,提出了快速适应的少数派博弈模型,仿真结果显示,新的模型有着和真实市场相同的特征:收益率分布的尖峰和肥尾现象,揭示了资本市场复杂性的内部动力学机理.     

Cross‐Milieu Terrorist Collaboration: Using Game Theory to Assess the Risk of a Novel Threat

This article uses a game‐theoretic approach to analyze the risk of cross‐milieu terrorist collaboration—the possibility that, despite marked ideological differences, extremist groups from very different milieus might align to a degree where operational collaboration against Western societies becomes possible. Based upon theoretical insights drawn from a variety of literatures, a bargaining model is constructed that reflects the various benefits and costs for terrorists’ collaboration across ideological milieus. Analyzed in both sequential and simultaneous decision‐making contexts and through numerical simulations, the model confirms several theoretical arguments. The most important of these is that although likely to be quite rare, successful collaboration across terrorist milieus is indeed feasible in certain circumstances. The model also highlights several structural elements that might play a larger role than previously recognized in the collaboration decision, including that the prospect of nonmaterial gains (amplification of terror and reputational boost) plays at least as important a role in the decision to collaborate as potential increased capabilities does. Numerical simulation further suggests that prospects for successful collaboration over most scenarios (including operational) increase when a large, effective Islamist terrorist organization initiates collaboration with a smaller right‐wing group, as compared with the other scenarios considered. Although the small number of historical cases precludes robust statistical validation, the simulation results are supported by existing empirical evidence of collaboration between Islamists and right‐ or left‐wing extremists. The game‐theoretic approach, therefore, provides guidance regarding the circumstances under which such an unholy alliance of violent actors is likely to succeed.     

Total Economic Consequences of an Influenza Outbreak in the United States

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state‐of‐the‐art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.     

Evaluation of multi-objective optimization approaches for solving green supply chain design problems

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO₂ and fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker׳s (DM׳s) indirect preferences into the search process. Within this case study, the CO₂ emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased.     

光滑暧昧模型下的不透明交易和管制措施研究

本文假定透明交易者对额外投资机会回报率的标准差(方差,投资风险)存在暧昧,这种认知暧昧性抑制了透明交易者的投资行为,会导致风险资产溢价过高及社会福利损失.透明交易者是暧昧厌恶的投资者,其投资决策依据光滑暧昧厌恶模型,需求函数呈现连续且光滑的特征.而不透明交易者,通过支付一定的信息获取成本获得私有信息而具有信息优势,他们是标准的风险厌恶的投资者.通过构建理性预期均衡,本文的研究发现:初始资产严格为正的透明交易者将获得严格为正的超额收益;提高信息获取成本将减少不透明交易者的比例,从而增加风险资产溢价,降低福利水平,因而不是一项好的管制措施;而旨在提高市场透明度降低交易者暧昧性的举措总有利于提高福利水平.     

指令驱动市场中非知情交易者的最优交易策略

在非对称信息下,综合非知情交易者对资产价值的预期、私人估值及资产价值波动,构造指令驱动市场价格形成的动态模型,给出非知情交易者指令提交策略的解析解,并依此描述了非知情交易者对资产价值预期的动态更新过程.研究发现:私人估值和资产价值波动通过影响非知情交易者最优策略,使得市场进入不同均衡状态;在每个均衡状态下,非知情交易者根据资产价值的预期变化更新交易策略,进而引起知情交易者策略调整,特别地,使得知情交易者被挤出市场成为可能.进一步分析表明,私人估值、资产价值波动及知情交易者比例对市场流动性的影响依赖于每个均衡中限价指令执行风险的差异.     

基于分量差的线性及匿名合作对策值的简化算法

通过给出Shapley值、均分Shapley值、贴现Shapley值、Solidarity值、广义Solidarity值、合意值、 Banzhaf值及最小二乘预核仁分量差的显式解析表达式,本文提出了一种同时计算这些线性及匿名效用可转移合作对策值的简化算法.特别地,这一算法也适用于同时计算这些值中的两种及以上.为了详细说明简化算法的计算过程及优越性,文中给出了具体的数值算例,并将其与传统算法进行了比较分析,结果表明简化算法确实能显著降低同时计算多个值的时间复杂度.