Stability under learning: The endogenous growth problem

A local dynamic analysis, in the neighborhood of the steady state, is developed for one and two-sector endogenous growth models. The problem differs from the conventionally assumed growth setups because one considers that expectations concerning the next period value of the control variable (consumption) are formed through adaptive learning. In such scenario, the found stability conditions reveal that convergence to the unique steady state point is feasible if a minimum requirement regarding the quality of learning in the long run equilibrium is fulfilled. Therefore, stability of growth under learning is dependent on the efficiency with which expectations are generated.     

A Markov analysis of social learning and adaptation

A number of recent contributions to the literature have modelled social learning and adaptation in an economic context. Understanding the processes driving these models is important in order to explain and predict the behaviour of the economy. In this paper, we analyze the economic applications for a class of adaptive learning models with bounded rational agents. The dynamics of these economies can be thought of as arising from discrete-time Markov chains. In particular, conditions for uniqueness of equilibria, convergence and stability in the economic systems follow from the accessibility and communication structures of these Markov chains. We establish a correspondence between absorbing states of the Markov chains and economic equilibria, whether stable or unstable, and develop theorems giving conditions for absorption and recurrence. Furthermore, we develop practical applications of these theorems using a cobweb model. We use a genetic algorithm, operating under election, as an example of a well known adaptive learning process.     

Asset pricing with adaptive learning

We study the extent to which self-referential adaptive learning can explain stylized asset pricing facts in a general equilibrium framework. In particular, we analyze the effects of recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We find that (a) recursive least squares learning has almost no effects on asset price behavior, since the algorithm converges relatively fast to rational expectations, (b) constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy but (c) in the production economy the effects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that in the context of these two commonly used models, standard linear self-referential learning does not resolve the asset pricing puzzles observed in the data.     

Heterogeneous beliefs and learning in forward looking economic models*

We study a class of forward looking economic models with heterogeneous agents in a bounded rationality setting. The agents employ the same recursive learning rule to update beliefs but are characterized by different memory parameters. The peculiarity of the learning mechanism is that the learning rate is not vanishing in the limit. Differently from what is obtained in the case of a vanishing learning step, i.e., the stability conditions in the heterogeneous agents case are those of the representative agent model, we show that heterogeneity matters for the expectational stability of a stationary perfect foresight equilibrium and that the stability parameter restrictions with heterogeneous agents are stronger than in the case of homogeneous agents.     

异质性条件下产品创新的最优市场结构研究

何种市场结构更有利于创新是经济学领域长期争论的话题。与传统文献的视角不同,本文从产品异质性的角度出发,同时兼顾原产品间以及新产品与原产品间的不完全替代性,构建理论模型分析不同类型的市场结构对于产品创新的影响。研究发现:那些产品差异度小、或者创新产品潜在利润高、或者创新产品对原产品的替代度低的产业,行业垄断程度越高越有利于创新;那些产品差异度大、或者创新产品潜在利润低、或者创新产品对原产品的替代度高的产业,行业竞争程度越高越有利于创新;那些参数处于中度值的产业,市场结构与创新的关系则呈倒U型结构。本文的最后对如何有效利用产业政策促进产品创新进行了分析,并对中国创新的市场结构进行了反思。     

Expectations and learning under alternative monetary regimes: an experimental approach

Summary We design and analyze experimental versions of monetary overlapping generations economies under alternative policy regimes. Economies with a constant level of real deficit financed through seignorage, economies in which the level of deficit is adapted in order to follow a monetary policy with a target rate of inflation, and economies with preannounced changes in deficit levels are reported here. We also examine the behavior of an economy with no stationary competitive equilibrium. Our time series are compared to rational expectations equilibrium paths and to adaptive learning dynamics.Financial support for our work from the Graduate School of the University of Minnesota, the National Science Foundation (SES-8912552), Richard M. and Margaret Cyert Family Funds, and the Ministry of Education of Spain is gratefully acknowledged. We wish to thank Javier Diaz-Giménez, Jean Michael Grandmont, Robert Lucas, Albert Marcet, Charles Plott, Edward Prescott, Thomas Sargent, Neil Wallace and Michael Woodford for commenting on our work. We also thank Vijay Rajan for developing the software for the computerized experimental environment, and Jackie Cuccaro and Dhananjay Gode for their research assistance.     

Unemployment and econometric learning

We apply well-known results of the econometric learning literature to the Mortensen and Pissarides real business cycle model. Agents can always learn the unique rational expectations equilibrium (REE), for all possible well-defined sets of parameter values, by using the minimum-state-variable solution to the model and decreasing gain learning. From this perspective the assumption of rational expectations in the model could be seen as reasonable. But using a parametrisation with UK data, simulations show that the speed of convergence to the REE is slow. This type of learning dampens the cyclical response of unemployment to small structural shocks.     

学习型组织、学习型领导、学习力

21世纪是知识经济时代,在知识经济时代,成功只代表着过去,能力代表着今天,学习力代表着未来。个人要成为学习者,企业要成为学习型组织,国家要成为学习型社会。新世纪企业领导的主要任务是领导企业的员工更好的学习,提高企业的学习能力,学习型组织的领导者也有了新的角色定位,领导者是设计师、仆人和教师。员工素质和水平提高了,领导的水平更应该提高,提高的一个主要途径就是提升学习力。一个具有学习能力的人,就是一个不断走向成功的人。21世纪成功的关键是要进行有效的学习,即爱学、会学才会赢。每家企业都会有不同的“学习途径”,可谓条条道路通罗马。     

Naive learning and cooperation in network experiments

In this paper we study learning and cooperation in repeated prisoners' dilemmas experiments. We compare interaction neighbourhoods of different size and structure, we observe choices under different information conditions, and we estimate parameters of a learning model.We find that naive imitation, although a driving force in many models of spatial evolution, may be negligible in the experiment. Naive imitation predicts more cooperation in spatial structures than in spaceless ones—regardless whether interaction neighbourhoods have the same or different sizes in both structures. We find that with some interaction neighbourhoods even the opposite may hold.     

Overconfidence and Market Efficiency with Heterogeneous Agents

We study financial markets in which both rational and overconfident agents coexist and make endogenous information acquisition decisions. We demonstrate the following irrelevance result when a positive fraction of rational agents (endogenously) decides to become informed in equilibrium, prices are set as if all investors were rational, and as a consequence the overconfidence bias does not affect informational efficiency, price volatility, rational traders’ expected profits or their welfare. Intuitively, as overconfidence goes up, so does price informativeness, which makes rational agents cut their information acquisition activities, effectively undoing the standard effect of more aggressive trading by the overconfident. The main intuition of the paper, if not the irrelevance result, is shown to be robust to different model specifications.We would like to thank Alberto Bisin, Xavier Freixas, Ken French, Moshe Kim, Jose Marín, and Terrance Odean for comments on an early draft, as well as an anonymous referee and seminar participants at HEC Geneva, the 2004 EFA meetings, the 2004 European Econometric Society meetings and the 2005 SAET conference. Diego García and Branko Urošević gratefully acknowledge financial support by SECCF (Belgrade).     

合作创新网络的异质性要素识别研究

任何企业都不具备实现整个价值链中所有活动所需要的全部资源,创新所需资源的获取主要是通过网络组织实现资源的跨企业流动,如何识别并获取异质性资源,是企业实现持续创新的关键问题。在此基础上,编制合作创新网络异质性量表。基于合作创新理论与合作创新网络异质性要素的理论研究,对国际上已有量表进行分析,开发出合作创新网络异质性量表。先参考已有研究选取20个题项测度合作创新网络异质性,对458家企业开展深度访谈和问卷调查,再通过SPSS和AMOS等软件对样本数据进行探索性因子分析和验证性因子分析,最后对其信度效度进行检验。研究结果表明,合作创新网络异质性量表具有良好的信度和效度,量表包括三个关键异质性要素:技术多样性、伙伴异质性以及地理分散性。     

Equilibrium play and adaptive learning in a three-person centipede game

The two-person centipede game is one of the most celebrated paradoxes of backward induction in complete information extensive form games. An experimental investigation of a three-person centipede game shows that the paradoxical results are strongly affected by the size of the stakes. When the number of players in the game is increased from two to three and the game is played for unusually high stakes with group composition being randomly changed from trial to trial, the paradox is considerably weakened as players approach equilibrium play with multiple iterations of the stage game. When the game is played with low stakes, there is no evidence for equilibrium play or learning across iterations of the stage game. An adaptive learning model that assumes updating of the individual probabilities of choice outperforms alternative static and dynamic models in accounting for the major results observed in the high-stake experiment.     

财政分权与市场整合对 工业用地利用效率影响的异质性研究

本文利用2005—2017年省际面板数据,运用SBM-DEA模型测算中国30省份（西藏地区和港澳台地区因数据缺失而剔除）工业用地利用效率。随后,构建面板分位数回归模型,研究财政分权与市场整合在不同分位点处对工业用地利用效率的异质性影响。研究发现：第一,当工业用地利用效率较低时（0.1~0.3分位点处）,财政分权对工业用地利用效率具有显著的负向影响（影响系数为－0.6402~－0.1998）;而随着分位点提升（0.4~0.9分位点处）,财政分权对工业用地利用效率将产生正向促进作用（影响系数为0.2301~0.7150）;财政分权对工业用地利用效率增长率的影响随着分位点提高大致呈倒“U”型变化趋势。第二,随着分位点的提高,市场整合对工业用地利用效率增长的贡献趋于平稳,对工业用地利用效率增长率的正向促进则逐渐减弱。第三,财政分权与市场整合的共同作用在低分位点处对工业用地利用效率及其增长率具有负向影响（对工业用地利用效率的影响系数为－0.0294~－0.0088;对工业用地利用效率增长率的影响系数为－0.2635~－0.2838）,在高分位点处则会产生正向促进作用（对工业用地利用效率的影响系数为0.0042~0.0540;对工业用地利用效率增长率的影响系数为0.2004~1.1496）。     

《课程要求》下的外语学习策略训练

《：大学英语课程教学要求（试行）》将学习策略作为大学英语教学的主要内容之一,因此,我们有必要探讨学习策略在外语教学中的重要性,分析策略训练中应注意的问题,以适应大学英语教改的要求,从而提高学生的自主学习能力。     

环保PPP项目异质行动者网络构建研究

现有环保PPP项目多元主体研究缺乏对环境等非人类主体的关注。因此,在分析多元主体异质性基础上,引入行动者网络理论,清晰界定人类行动者和非人类行动者在环保PPP项目中的角色与地位,通过异质行动者转译,构建环保PPP项目异质行动者网络,清除异质行动者的行动障碍,实现异质行动者间的资源互补,提高环保PPP项目供给效率,为多元主体异质合作供给环保PPP项目研究提供新思路。     

Learning and international transmission of shocks

This paper studies the implications of adaptive learning in the modelling of inter-country linkages in a two-region MSG G-cubed model built on micro-founded behaviors of firms and households. The nature of the transmission process under rational expectations versus adaptive learning is explored. We investigate the propagation mechanism within and across borders for various shocks and policy changes within the United States: change in inflation target, fiscal policy, productivity shock, and rise in equity risk. Adaptive learning is found to change the short run sign of transmission in most cases but this also depends on the fraction of forward-looking agents in the economy.     

Sabotage in Tournaments with Heterogeneous Contestants: Empirical Evidence from the Soccer Pitch

In this paper, we address the problem of sabotage in tournaments with heterogeneous contestants. In a first step, we develop a formal model, which yields the prediction that favorites exert higher productive effort, while underdogs are more tempted to engage in destructive actions (sabotage). This is because favorites have a higher return on productive effort and both types of effort are substitutes. In a second step, we use data from German professional soccer to test this prediction. In line with the model, we find that favorite teams win more tackles in a fair way, while underdog teams commit more fouls.     

Coordination and organizational learning in the firm

This paper discusses the role of the organizational structure in shaping the organizational learning process. Learning is modelled by means of a computational model in which search takes place in the space of problem representations and cannot be reduced to mere probability updating within a given and constant representation.When the assumption of a unique and given representation of the problem is dropped, organizational learning emerges from the coordination of individual learning processes. Some simulations analyze the performance, in different environmental conditions, of centralized and decentralized coordination modes.I would like to thank Giovanni Dosi, Keith Pavitt, Nick von Tunzelmann and two anonymous referees for their insightful suggestions. An earlier version of this paper was presented at the Third Meeting of the Joseph A. Schumpeter Society, Airlie House, Virginia, June 1990.