Two Competing Models of How People Learn in Games

Reinforcement learning and stochastic fictitious play are apparent rivals as models of human learning. They embody quite different assumptions about the processing of information and optimization. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator dynamics. Therefore they will in many cases have the same asymptotic behavior. In particular, local stability of mixed equilibria under stochastic fictitious play implies local stability under perturbed reinforcement learning. The main identifiable difference between the two models is speed: stochastic fictitious play gives rise to faster learning.     

An Evaluation of Econometric Models of Adaptive Learning

This paper evaluates the effectiveness of four econometric approaches intended to identify the learning rules being used by subjects in experiments with normal form games. This is done by simulating experimental data and then estimating the econometric models on the simulated data to determine if they can correctly identify the rule that was used to generate the data. The results show that all of the models examined possess difficulties in accurately distinguishing between the data generating processes.     

Stochastic Algorithms,Symmetric Markov Perfect Equilibrium,and the ‘curse’ of Dimensionality

This paper introduces a stochastic algorithm for computing symmetric Markov perfect equilibria. The algorithm computes equilibrium policy and value functions, and generates a transition kernel for the (stochastic) evolution of the state of the system. It has two features that together imply that it need not be subject to the curse of dimensionality. First, the integral that determines continuation values is never calculated; rather it is approximated by a simple average of returns from past outcomes of the algorithm, an approximation whose computational burden is not tied to the dimension of the state space. Second, iterations of the algorithm update value and policy functions at a single (rather than at all possible) points in the state space. Random draws from a distribution set by the updated policies determine the location of the next iteration's updates. This selection only repeatedly hits the recurrent class of points, a subset whose cardinality is not directly tied to that of the state space. Numerical results for industrial organization problems show that our algorithm can increase speed and decrease memory requirements by several orders of magnitude.     

具有灰色随机动态特征的供应链牛鞭效应的鲁棒性分析

供应链的随机动态性和牛鞭效应加大了供应链系统建模鲁棒性的难度,供应链的平稳运行是供应链管理的重要基础。针对具有区间灰色特征的随机动态供应链系统,以线性时不变系统作为研究基础,提出使用Markov算法解决供应链系统随机线性跳变的鲁棒性问题,获取了判定随机动态供应链系统鲁棒性的一个有效度量指标。算例结果表明:随机动态供应链系统的牛鞭放大效应与节点间的变化幅度和长期概率的平均值均无关;Markovian跳变线性供应链系统的整体性能不能仅凭单个节点的性能来预测;供应链运作的不确定性将会导致系统整体性能的降低。     

考虑随机市场需求的双渠道供应链风险补偿策略研究——从质量与价格竞争视角

在由两个供应商和单个零售商构成的二级双渠道供应链系统中，分析了随机市场需求以及供应商之间同时存在价格与质量竞争的情形下的双渠道供应链协调问题。在供应链集中决策、无风险补偿及有风险补偿三种情境下，构建了基于质量和价格的风险补偿模型并求得纳什均衡解。研究结果表明：存在可行的需求风险补偿策略使得供应链达到协调水平，并且需求风险补偿策略对零售商更加有利；在需求风险补偿策略下，需求风险补偿价格与其对应批发价格正相关，与其产品质量水平负相关；价格竞争程度对零售商订货量具有负向影响作用，对供应链总利润影响作用随着竞争程度增加而减弱，质量竞争程度对零售商订货量和供应商产品质量水平具有正向影响作用，对供应链总利润影响呈倒U型关系；需求风险补偿策略能够激励零售商的订货行为，强化价格竞争程度和质量竞争程度对供应商订货量和供应链总利润的影响；在供应链系统中双渠道营销模式下的供应链总利润要优于单渠道营销模式下的供应链总利润。本文结论不仅详细剖析了风险补偿策略对双渠道供应链协调的影响关系，也理清了价格与质量竞争对各方行为策略的影响机理。     

Understanding managerial propensity to participate in learning activities: the case of the Scottish life assurance industry

Against a background of continuing upheaval and change (increased competition and technological advances), an analysis of the barriers to participation in learning was undertaken through in-depth interviews with a sample of 61 mid-career managers from five of the largest life assurance institutions in Scotland. Utilising a 'grounded theory' methodology, a 'Model of Propensity to Participate in Learning Activities' was developed to determine the prevalence of both intrinsic (aspects associated with the individual learner) and extrinsic (aspects associated with the organisation or industry in which the learner operates) factors affecting participation in learning. Five intrinsic variables (Perceptual, Emotional, Motivational, Cognitive (General) and Cognitive (Vocational)) and three extrinsic variables (Culture, Management Development Culture and Physical Resources) were proposed, against which the managers were categorised according to their propensity to participate in learning initiatives.