Social learning in networks: a Quantal Response Equilibrium analysis of experimental data

Individuals living in society are bound together by a social network and, in many social and economic situations, individuals learn by observing the behavior of others in their local environment. This process is called social learning. Learning in incomplete networks, where different individuals have different information, is especially challenging: because of the lack of common knowledge individuals must draw inferences about the actions others have observed, as well as about their private information. This paper reports an experimental investigation of learning in three-person networks and uses the theoretical framework of Gale and Kariv (Games Econ Behav 45:329–346, 2003) to interpret the data generated by the experiments. The family of three-person networks includes several non-trivial architectures, each of which gives rise to its own distinctive learning patterns. To test the usefulness of the theory in interpreting the data, we adapt the Quantal Response Equilibrium (QRE) model of Mckelvey and Palfrey (Games Econ Behav 10:6–38, 1995; Exp Econ 1:9–41, 1998). We find that the theory can account for the behavior observed in the laboratory in a variety of networks and informational settings. This provides important support for the use of QRE to interpret experimental data.     

人事档案数字化建设策略研究

当今时代是数字化信息时代,其影响力已渗入多个产业领域。人事档案作为个体的一种重要的具有社会价值的信息资源,其包含了个体成长、学习、工作等多种信息,其重要性是值得关注的。不同的单位都会有相应的储存人事档案的场所和相对应的管理人员。数字化建设发展对于人事档案工作的发展是至关重要的,无论是工作效率的提高、档案利用率的提升,还是安全服务性的改善都值得推动其建设性发展。     

The economics analysis of a <Emphasis Type="Italic">Q</Emphasis>-learning model of cooperation with punishment and risk taking preferences

The aim of this paper is to better understand how cooperation mechanisms work in the context of a Q-learning model. We apply a learning reinforcement model to analyse the conditions needed to have a stable cooperative equilibrium when people take part in a common project and could take advantages of free-riding. Our results show that a stable equilibrium can be reached thank to mechanisms of punishment, but the final result strongly depends on the risk-taking individuals’ preferences. In particular, we find that the penalties will be effective only with people having high exploration rates,namely with people able to adapt their strategies and learn to cooperate. Otherwise, it is possible to have an unstable equilibrium with cooperation until individuals have a very high intrinsic motivation to cooperate, whatever the others do.     

Learnability of an equilibrium with private information

This paper investigates the learnability of an equilibrium with private information. Agents of each type have their own private information about an exogenous variable and conduct adaptive learning with a heterogeneously misspecified perceived laws of motion (PLM) that includes only this variable. The paper shows that the existence of private information has a nonnegative impact on the learnability of the equilibrium; that is, the condition for learnability is unaffected or relaxed by heterogeneity and/or misspecification in PLMs caused by private information. In a New Keynesian model with private information about fundamental shocks, the learnability of the equilibrium is ensured by the Taylor principle of monetary policy. The paper also confirms that these results hold true not only in the presence of private information, but also in a variety of informational structures.     

How to learn new tasks: Shop floor performance effects of knowledge transfer and performance feedback

We investigate how learning and the task performance of individuals are affected by different forms of knowledge transfer. Whereas previous research has proven the positive performance impacts of knowledge transfer, self-observation and feedback mechanisms individually, we explore the cumulative effect of these factors on learning and performance. With the help of two laboratory experimental studies reproducing manufacturing tasks that are typical for industrial production, we show that explicit knowledge transfer is superior to other forms of knowledge transfer. Externally provided performance feedback in the form of cost information and non-financial performance indicators has no effect on the order of different forms of knowledge transfer. Moreover, external feedback does not even have an additional significant performance effect on learning new tasks irrespective of the type of knowledge transfer.     

Transforming your manufacturing organization into a learning organization.

Many experts believe the only sustainable advantage an organization will have in the future is its ability to learn faster than its competitors. This competitive advantage can be achieved by transforming the organization into a learning organization. This article describes the basic elements of a learning organization and how to transform an organization to focus on learning. The article also describes methods for evaluating the training and learning that occurs within the organization to ensure that training dollars are wisely spent. Finally, the article describes what individuals can do to focus on their own learning and personal development within an organization.     

Collective learning in clusters: Mechanisms and biases

Although collective learning has long been considered a core feature of successful clusters, many researchers have treated the concept of learning more as a metaphor than a construct that requires an understanding of the various processes and mechanisms involved. I draw on the cultural-evolutionary perspective to argue that learning in clusters is an inherently biased process, with outcomes that can be both functional and dysfunctional. The cultural-evolutionary approach views learning as a process of imitation, treats beliefs as the unit of selection, and considers individuals as agents who are limited in their cognitive capabilities and social autonomy. Using interview data on 62 small business owners and 34 institutional actors in a textile and a surgical instruments cluster in South-west Germany, I show that the learning process can involve social biases which, in these cases, have the effect of reproducing a collective mindset built on distrust and rivalry. The findings provide an explanation for the fact that many studies of clusters have not been able to document the high levels of interfirm collaboration that cluster theory predicts.     

The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market

This paper investigates the formation of prices in a perishable goods market where agents bargain repeatedly through pair-wise interactions. After extensive field observations, we chose to focus on two aspects that seem important to actors of this market: the passage of time and update in judgement when gathering information. The main feature of the market is that a seller bargaining with a buyer has incomplete information about buyer's willingness to pay and is not sure how her trading partner will evaluate an offer or compare it with other options. On the other hand, buyers have limited time to look for goods and cannot meet all possible sellers before making a decision. Hence agents cannot calculate the best price to offer but receive information through limited interactions, and use this information to choose their actions.An agent-based model was built to represent a framework that mimics the observed market institution and where agent's possible behaviors and learning was made as consistent as possible with gathered data. Simulations were run, first for sensitivity analysis concerning main parameters, then to test the dependance of agents’ learning to (a) the time buyers can spend on the market and (b) the frequency of update in learning by sellers. To validate the model, features produced by the simulated market are compared to the stylized facts gathered for negotiation about four goods. We reproduce the main features of the data on the dynamics of offers, transaction prices and agents’ behavior during the bargaining phases.     

Managerial expertise,learning potential and dynamic incentives: get more for less?

In this paper the impact of ability and learning potential on incentive contracts is analyzed. A central feature of the model is that the true ability will not be revealed. The learning potential of an agent is modeled as the magnitude of impact on the agent's expected ability that learning‐by‐doing has in a given task. Absent a managerial labor market, depending on an agent's learning potential, a monotone or non‐monotone pay structure may be optimal. The second important result is that using agents' ability distributions as inputs to information systems, higher learning potentials lead to less costly information systems, i.e. actions can be implemented at lower costs. Additionally, it is proven that the criteria cost minimization and value maximization are equivalent in the model's context. Copyright © 2007 John Wiley & Sons, Ltd.     

Building External Corporate Venturing Capability*

ABSTRACT How firms build new capabilities to adapt to changing environments is at the core of strategic management. However, research has addressed this question only recently. In this paper, I propose a model that describes how firms develop a capability to create and develop ventures through corporate venture capital, alliances, and acquisitions. The model is based on two longitudinal case studies of large corporations operating in the information and communication technology sector in Europe. At the core of this model are learning processes that enable the firm to build up an external corporate venturing capability, by utilizing learning strategies both within and outside venturing relationships. To build this new capability, firms engage in acquisitive learning. Critical to deepening the capability acquired is adaptation of all knowledge to the firm specific context through experiential learning mechanisms. I also discuss the important role that initial conditions and knowledge management practices play in determining the direction and effectiveness of specific learning processes that lead to an external corporate venturing capability.     

Team Formation and Self-serving Biases

There is extensive evidence which indicates that people learn positively about themselves. We build on this finding to develop a model of team formation. We show that under complete information learning positively about oneself prevents efficient team formation. Agents becoming overconfident tend to ask for an excessive share of the group outcome. Positive learning generates divergence in workers' beliefs and hampers efficient team formation. Interestingly, in a context of incomplete information regarding the partner's ability, extensive learning biases may reduce the divergence in agents' beliefs and facilitate efficient team formation as a result. We apply our model to coauthorship and organizational issues.     

Self-organization and the persistence of noise in financial markets

A dynamic model of financial markets with learning is demonstrated to produce a self-organized system that displays critical behavior. The price contains private information that traders learn to extract and employ to forecast future value. Since the price reflects the beliefs of the traders, the learning process is self-referencing. As the market learns to correctly extract information from the price, the market deemphasizes private information. Despite the convergence of the model towards the parameters producing efficiency, pricing deviations remain constant due to the increased sensitivity of the price to small errors in information extraction produced by the model's own convergence.     

Imperfect information and the house price in a general-equilibrium model

House prices have inertia, which may be because housing-market participants need time to recognize long booms and recessions. Within a dynamic stochastic general-equilibrium model with an endogenous market for housing, I consider the case of rational expectations subject to imperfect information about the persistence of exogenous shocks. I evaluate the performance of the model against the last 40 years of key U.S. macroeconomic data. Bayesian comparison strongly favors the model over the baseline case with perfect information. Under imperfect information, agents rely on learning to form expectations, which improves the ability of the model to generate realistic low-frequency house-price dynamics. However, as long as the agents form expectations rationally, the improvement is limited. Furthermore, to confine price inertia within the housing market is a challenge for the general-equilibrium approach.     

跨团队成员对个人和团队学习力及生产力的影响

跨团队成员作为科层组织外衍生的纵向混合组织,在新产品开发、降低成本及提高组织运营效率等方面,均发挥着积极作用。跨团队成员数量和团队多样性,对团队和个人的生产力和学习力有着不可忽略的影响。因此,企业应该平衡跨团队成员的数量和团队多样性,提高人力资源的周转率和利用率,促进知识共享,从而提高团队和个人的生产力和学习力。为管理跨团队成员提供了全新的思路。     

Is Innovation Dependent on the Hierarchical Leadership Pyramid?

The purpose of the current essay is to consider the influence that the hierarchical form of leadership has on the ability to innovate within an organization. Organizational leadership sets the direction that steers innovation. Leadership facilitates the collaborative efforts of a diverse group of individuals. Leadership also provides the support and resources necessary to implement innovative initiatives successfully. The leadership develops the culture that can either help or hinder innovation. A culture that supports innovation emphasizes learning, experimenting, diversity, and collaboration. Leadership encourages either a mindset of collaboration or that of competition. Ideas and problems should be challenged in positive, collaborative ways rather than by competitive methods. Individuals in a position of authority must understand the impact that their support and access to resources have on the process of innovation. Such an understanding will allow leadership to focus their energy on providing a clear direction and sharing information, supporting implementation, building an innovative culture, and developing an organizational mindset of collaboration not competition.     

Influences On Knowledge processes In Organizational Learning: The Psychosocial Filter

This paper reports a segment of broader theory-building case study research exploring organizational learning and knowledge processes in a bio-medical consortium. Its focus is the individual-level factors that influence knowledge processes associated with organizational learning. As we explored how rganizational learning occurred, the underlying knowledge processes came forward as complex and idiosyncratic. In an unanticipated finding, micro-processes emerged as highly influential, with individual perceptions of approachability, credibility and trustworthiness mediating knowledge importing and knowledge sharing activities. We introduce a model – the psychosocial filter – to describe the cluster of micro-processes that were brought forward in the study. Firstly, scientists filtered knowledge porting by deciding whom they would approach for information and from whom they would accept input. The individual's confidence to initiate information requests (which we termed social confidence) and the perceived credibility of knowledge suppliers both mediated knowledge importing. Secondly, scientists mediated knowledge sharing by actively deciding with whom they would share their own knowledge. Perceived trustworthiness – based on perceptions of what olleagues were likely to do with sensitive information – was the factor that influenced knowledge-sharing decisions. Significantly, the psychosocial filter seemed to constitute a heedful process with high functionality. Its effect was not to block knowledge circulation, but instead to ensure that nowledge-sharing decisions were made in a thoughtful and deliberate way. The psychosocial filter suggests an initial framework for conceptualizing the role that individual-level processes play in organizational knowledge sharing. Building on this, the model provides a platform for more focused exploration of knowledge processes and social relationships in organizational learning.     

Learning and coordination with dispersed information

We analyse adaptive learning in a model of incomplete and dispersed information, with externalities and strategic interactions. We build on the framework proposed by Angeletos and Pavan (2007a) and extend it to a dynamic multi-period setting where agents need to learn to coordinate. We derive conditions under which adaptive learning obtains in such setting and show that, when actions are strategic substitutes, the information structure affects the speed of convergence: while more precise private information is beneficial, better public information has negative effects. We also show that adaptive learning dynamics converge to the Bayesian Nash equilibrium, which means that agents can learn to act strategically by relying only on observable (exogenous) information.     

Refined analysis and a hierarchical multi-task learning approach for loan fraud detection

Fraud problems in loan application assessment cause significant losses for finance companies worldwide, and much research has focused on machine learning methods to improve the efficacy of fraud detection in some financial domains. However, diverse information falsification in individual fraud remains one of the most challenging problems in loan applications. To this end, we conducted an empirical study to explore the relationships between various fraud types and analyzed the factors influencing information fabrication. Weak relationships exist among different falsification types, and some essential factors play the same roles in different fraud types. In contrast, others have various or opposing effects on these types of frauds. Based on this finding, we propose a novel hierarchical multi-task learning approach to refine fraud-detection systems. Specifically, we first developed a hierarchical fraud category method to break down this problem into several subtasks according to the information types falsified by customers, reducing fraud identification's difficulty. Second, a heterogeneous network with a meta-path-based random walk and heterogeneous skip-gram model can solve the representation learning problem owing to the sophisticated relationships among the applicants' information. Furthermore, the final subtasks can be predicted using a multi-task learning approach with two prediction layers. The first layer provides the probabilities of general fraud categories as auxiliary information for the second layer, which is for specific subtask prediction. Finally, we conducted extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed approach.     

Experimental evidence on trading behavior,market efficiency and price formation in double auctions with unknown trading duration

The reasons for the highly efficient market outcomes observed under the double auction remain unclear. This paper presents a series of experimental financial markets designed to investigate the importance of unknown trading period duration on trading behavior and the convergence tendencies of such markets. Using panel data techniques the results support the conclusions that individuals generally display more aggressive trading strategies, trading earlier in a period, and that markets exhibit reduced levels of informational efficiency when unknown duration is present. Markets with imperfect information structures are also studied and, in a unique result, are associated with significantly slower rates of trade, as traders become more cautious over their trading strategies. Investigation of the price formation process provides evidence that the pricing error varies over time and the estimation of a fixed effects model provides unique support that learning effects and unknown trading period duration influence the price formation process. Future refinement of theoretical models of the price formation process or institutions of exchange should recognize the effect of unknown trading period duration on market behavior, along with potential learning effects. Copyright © 2005 John Wiley & Sons, Ltd.     

Adaptive learning in an asymmetric auction: genetic algorithm approach

Agent-based simulations are performed to study adaptive learning in the context of asymmetric first-price auctions. Non-linearity of the Nash equilibrium strategies is used to investigate the effect of task complexity on adaptive learning by varying the degree of approximation the agents can handle. In addition, learning in different information environments is explored. Social learning allows agents to imitate each other’s bidding strategies based on their relative success. Under individual learning agents are limited to their own experience. We observe convergence to steady states near the predicted equilibrium in all cases. The ability to learn non-linear functions helps the agents with a non-linear equilibrium strategy but hurts the agents with an almost linear one. Better information about the opponent population has a relatively modest impact. A larger number of strategies to experiment with and an ability to systematically compare strategies by holding a number of factors constant have a comparatively stronger beneficial effect.