有限理性假设下供应链合作博弈的定性模拟研究

本文放宽一般博弈论中理性人的假设,运用混合建模方法构建了有限理性下的供应链合作博弈模型,模型将供应链企业的收益模糊化并扩展供应链合作博弈中局中人的策略空间,可以选择从“0”(完全不合作)到“1”(完全合作)连续区间内的任何合作程度,策略集是连续的区间.模拟结果表现为供应链合作策略的调整过程,能够提供更有效的决策支持.在此基础上,以二人合作博弈为例,用结合定性信息的定性模拟方法对博弈过程进行研究,得到结论:在供应链合作中,无论是平均分配还是按成员企业的资源投入分配,从模拟结果来看,定性模拟方法的使用,对一些不具备完备信息,无法建立精确数学模型的博弈过程也可以进行分析,并得出相对满意的结论.     

MODELLING COGNITIVELY BOUNDED RATIONALITY: AN EVALUATIVE TAXONOMY

Abstract Since its origins in the 1950s, the notion of bounded rationality has been incorporated into economic theory using an array of methodological approaches. This paper presents a taxonomy by which these approaches can be categorized and understood, evaluating each approach against a range of important criteria. At a time when the foundations of economic theory are being re‐examined, this paper should be viewed as a first step towards an assessment of the appropriate role that this more realistic view of cognition should take in different areas of economic theory, and also of the desirable characteristics of models that incorporate it.     

The heterogeneous expectations hypothesis: Some evidence from the lab

This paper surveys learning-to-forecast experiments (LtFEs) with human subjects to test theories of expectations and learning. Subjects must repeatedly forecast a market price, whose realization is an aggregation of individual expectations. Emphasis is given to how individual forecasting rules interact at the micro-level and which structure they cocreate at the aggregate, macro-level. In particular, we focus on the question wether the evidence from laboratory experiments is consistent with heterogeneous expectations.     

Imitators and optimizers in Cournot oligopoly

We analyze a symmetric n-firm Cournot oligopoly with a heterogeneous population of optimizers and imitators. Imitators mimic the output decision of the most successful firms of the previous round à la Vega-Redondo, F., [1997. The evolution of Walrasian behavior. Econometrica 65, 375–384]. Optimizers play a myopic best response to the opponents’ previous output. Firms make mistakes and deviate from their decision rules with a small probability. Applying stochastic stability analysis, we find that the long run distribution converges to a recurrent set of states in which imitators are better off than are optimizers.     

Fairness in supply chain contracts: A laboratory study

Various contracts can be designed to coordinate a simple supplier–retailer channel, yet the contracts proposed in prior research and tested in a laboratory setting do not perform as standard theory predicts. The supplier, endowed with all bargaining power, can neither fully coordinate the channel nor extract all of the channel profit. We report on a sequence of laboratory experiments designed to separate possible causes of channel inefficiency. The three causes we consider are inequality aversion, bounded rationality, and incomplete information. It turns out that all three affect human behavior. Inequality aversion has by far the most explanatory power regarding retailers’ behavior. Incomplete information about the retailer's degree of inequality aversion has the most explanatory power in regards to the suppliers’ behavior. Bounded rationality affects both players, but is of secondary importance.     

Bounded rationality,anchoring-and-adjustment sentiment,and asset pricing

This paper presents a framework that incorporates an investor’s limited attention and anchoring and adjustment sentiment and their joint effects on asset pricing, with endogenous cost of neglecting part of the dividends and the asymmetric rationality levels of investors. We find that the combined effect of the two bounded rationality factors is often embodied in the “loss”, and the retail investors are insensitive to market sentiment and forced to pay more cognitive loss. A higher level of investor rationality and bullish market sentiment will jointly increase demand and then prices, while the effects of different bounded rationality factors are asymmetric.     

COMPLEXITY AND RANDOMNESS IN MATHEMATICS: PHILOSOPHICAL REFLECTIONS ON THE RELEVANCE FOR ECONOMIC MODELLING

Abstract Mathematics itself is a complex system. It exemplifies complexity at the level of structure, hierarchy and so on. There is also an interesting notion of complexity present in the meaning of mathematical ‘alphabets’. These are unique writing strategies of mathematics. Yet another marker of complexity lies in the process of applying mathematics to models. Using mathematics in modelling is a process of deciding what kinds of models to construct and what types of mathematics to use. Modelling can be seen as a decision‐making process where the scientists are the agents. However in choosing mathematical structures the scientist is not being optimally rational. In fact, fertile uses of mathematics in the sciences show a complicated use of mathematics that cannot be reduced to a method or to rational principles. This paper argues that the discourse of satisficing and bounded rationality well describes the process of choice and decision inherent in modelling.     

Investment behavior under Knightian uncertainty – An evolutionary approach

The ‘as if ’ view of economic rationality defends the profit maximization hypothesis by pointing out that only those firms who act as if they maximize profits can survive in the long run. Recently, the problem of arriving at a logically consistent definition of rational behavior in games has shown that one must sometimes study explicitly the evolutionary processes that form the basis of this view. The purpose of this paper is to investigate the usefulness of genetic programming as a tool for generating hypotheses about rational behavior in situations where explicit maximization is not well defined. We use an investment decision problem with Knightian uncertainty as a borderline test case, and show that when the artificial agents receive the same information about the unknown probability distributions, they develop behavior rules as if they were expected utility maximizers with Bayesian learning rules.     

Auctions with asymmetric common-values: The first-price format

We study the performance of the first-price format in auctions with asymmetric common-values. We show that, contrary to the result for second-price auctions, a small advantage for one player translates only to small changes in bidders’ strategies, and the equilibrium remains close to the first-price equilibrium of the original game. We characterize the equilibrium bidding strategies and their behavior as the degree of asymmetry increases. Finally, we compare the revenues at the optimal auction, the first-price auction and the second-price auction.     

Linear learning in changing environments

The decision maker receives signals imperfectly correlated with an unobservable state variable and must take actions whose payoffs depend on the state. The state randomly changes over time. In this environment, we examine the performance of simple linear updating rules relative to Bayesian learning. We show that a range of parameters exists for which linear learning results in exactly the same decisions as Bayesian learning, although not in the same beliefs. Outside this parameter range, we use simulations to demonstrate that the consumption level attainable under the optimal linear rule is virtually indistinguishable from the one attainable under Bayes’ rule, although the respective decisions will not always be identical. These results suggest that simple rules of thumb can have an advantage over Bayesian updating when more complex calculations are more costly to perform than less complex ones. We demonstrate the implications of such an advantage in an evolutionary model where agents “learn to learn.”