Computing Normal Form Perfect Equilibria for Extensive Two‐Person Games

This paper presents an algorithm for computing an equilibrium of an extensive two‐person game with perfect recall. The method is computationally efficient by virtue of using the sequence form, whose size is proportional to the size of the game tree. The equilibrium is traced on a piecewise linear path in the sequence form strategy space from an arbitrary starting vector. If the starting vector represents a pair of completely mixed strategies, then the equilibrium is normal form perfect. Computational experiments compare the sequence form and the reduced normal form, and show that only the sequence form is tractable for larger games.     

Axiomatic Based Decomposition for Conceptual Product Design

This paper describes a structured methodology for decomposing the conceptual design problem in order to facilitate the design process and result in improved conceptual designs that better satisfy the original customer requirements. The axiomatic decomposition for conceptual design method combines Alexander's network partitioning formulation of the design problem with Suh's Independence Axiom. The axiomatic decomposition method uses a cross‐domain approach in a House of Quality context to estimate the interactions among the functional requirements that are derived from a qualitative assessment of customer requirements. These interactions are used in several objective functions that serve as criteria for decomposing the design network. A new network partitioning algorithm is effective in creating partitions that maximize the within‐partition interactions and minimize the between‐partition interactions with appropriate weightings. The viability, usability, and value of the axiomatic decomposition method were examined through analytic comparisons and qualitative assessments of its application. The new method was examined using students in engineering design capstone courses and it was found to be useable and did produce better product designs that met the customer requirements. The student‐based assessment revealed that the process would be more effective with individuals having design experience. In a subsequent assessment with practicing industrial designers, it was found that the new method did facilitate the development of better designs. An important observation was the need for limits on partition size (maximum of four functional requirements.) Another issue identified for future research was the need for a means to identify the appropriate starting partition for initiating the design.     

Heuristic Coordination of Decentralized Inventory Systems Using Induced Backorder Costs

In this paper, we investigate a one‐warehouse multiple‐retailer system, where the inventory control decisions are coordinated using a near optimal induced backorder cost, β*. All installations use continuous review installation‐stock (R, Q) policies. The analysis builds on an approximation model where the stochastic warehouse delays are replaced by their correct averages. The contributions include insights as to how β* is influenced by system parameters, and the determination of simple closed form β* estimates. The latter offering a practical means to achieve coordinated control of large size systems.     

The Speed of Experience: The Co‐narrative Method in Experience Economy Education

This paper proposes a management learning technique called the co‐narrative method. This approach is seen as a useful means of capturing the subtler nuances of experience economy interactions, as well as learning ethics and corporate social responsibility, by nurturing empathy and compassion. A method is presented based on the example of the idea of slow as fast side of organizational and festival experiences, which is explored through autoethnographic studies of participation in experience economy events. It builds upon insights into improving management education through the use of the humanistic approach. The so‐called co‐narrative method is based on a syzygic mode uniting the two oppositions (while preserving their inherent contradictions). It encourages its users to exercise understanding of the experience of the Other, while teaching about concrete cases and events.     

Lightning Rods,Earthquakes, and Regional Identities: Towards a Multi‐Scale Framework of Assessing Fracking Risk Perception

Hydraulic fracturing has provided a persistent, polarizing, and highly politicized source of controversy internationally and in numerous national contexts for just under a decade. This research uses hydraulic fracturing (i.e., fracking) operations in New Zealand as a vignette through which to understand the underlying causes of controversy and the appropriateness of attempts to address them. A multi‐method approach using interviews (n = 25), diagrammatic analysis, and newsprint media was applied to evidence two major findings. First, previous attempts to explain fracking controversy based on social constructivist theory lack a multi‐scalar approach to the assessment of factors that influence risk perceptions. It is found that risk perception surrounding fracking in New Zealand reflects intra‐scalar interactions between factors originating at the international, national, regional, and local scale. Second, there is a concerning absence of critique pertaining to the concept of “social license to operate” (SLO), which has been advocated both internationally and nationally as an appropriate form of stakeholder engagement. This article contributes to the SLO outcomes literature by establishing a need to consider multi‐scalar influences on risk perception when explaining diverse SLO outcomes in communities where fracking operations are prospective or already taking place.     

Exploring the Impact of Social Axioms on Firm Reputation: A Stakeholder Perspective

This study proposes a model of how deeply held beliefs, known as ‘social axioms, moderate the interaction between reputation, its causes and consequences with stakeholders. It contributes to the stakeholder relational field of reputation theory by explaining why the same organizational stimuli lead to different individual stakeholder responses. The study provides a shift in reputation research from organizational‐level stimuli as the root causes of stakeholder responses to exploring the interaction between individual beliefs and organizational stimuli in determining reputational consequences. Building on a conceptual model that incorporates product/service quality and social responsibility as key reputational dimensions, the authors test empirically for moderating influences, in the form of social axioms, between reputation‐related antecedents and consequences, using component‐based structural equation modelling (n = 204). In several model paths, significant differences are found between responses of individuals identified as either high or low on social cynicism, fate control and religiosity. The results suggest that stakeholder responses to reputation‐related stimuli can be systematically predicted as a function of the interactions between the deeply held beliefs of individuals and these stimuli. The authors offer recommendations on how strategic reputation management can be approached within and across stakeholder groups at a time when firms grapple with effective management of diverse stakeholder expectations.     

Estimating Social Influences from Social Networking Sites—Articulated Friendships versus Communication Interactions

Despite the ubiquity of social networking sites, the online social networking industry is in search of effective marketing strategies to better profit from their established user base. Social media marketing strategies build on the premise that the social network of online users can be predicted and social influences among online users can be estimated. However, the existence of various heterogeneous social interactions on social networking sites presents a challenge for social network prediction and social influence estimation. In this article we draw upon the literatures on self‐presentation on social networking sites and signaling in online social networking to categorize six heterogeneous online social interactions on social networking sites into two types—articulated friendships and communication interactions. This article provides empirical evidence for the differences between articulated friendships and communication interactions and the corresponding articulated and communication networks. In order to compare the impacts of the social influences based on these two networks, we utilize support vector machines to build a classifier to predict virtual community membership and we further estimate the marginal effects of these social influences using a two‐stage probit least squares method. We find significant explanatory power of social influences in predicting virtual community membership. Although the communication network is much sparser than the articulated network, social influences based on the communication network achieve similar performance as the articulated network. These findings provide important implications for social media marketing as well as the management of virtual communities.     

Impact of Number of Interactions,Different Interaction Patterns,and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.     

Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain

We develop results for the use of Lasso and post‐Lasso methods to form first‐stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p. Our results apply even when p is much larger than the sample size, n. We show that the IV estimator based on using Lasso or post‐Lasso in the first stage is root‐n consistent and asymptotically normal when the first stage is approximately sparse, that is, when the conditional expectation of the endogenous variables given the instruments can be well‐approximated by a relatively small set of variables whose identities may be unknown. We also show that the estimator is semiparametrically efficient when the structural error is homoscedastic. Notably, our results allow for imperfect model selection, and do not rely upon the unrealistic “beta‐min” conditions that are widely used to establish validity of inference following model selection (see also Belloni, Chernozhukov, and Hansen (2011b)). In simulation experiments, the Lasso‐based IV estimator with a data‐driven penalty performs well compared to recently advocated many‐instrument robust procedures. In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the Lasso‐based IV estimator outperforms an intuitive benchmark. Optimal instruments are conditional expectations. In developing the IV results, we establish a series of new results for Lasso and post‐Lasso estimators of nonparametric conditional expectation functions which are of independent theoretical and practical interest. We construct a modification of Lasso designed to deal with non‐Gaussian, heteroscedastic disturbances that uses a data‐weighted ℓ₁‐penalty function. By innovatively using moderate deviation theory for self‐normalized sums, we provide convergence rates for the resulting Lasso and post‐Lasso estimators that are as sharp as the corresponding rates in the homoscedastic Gaussian case under the condition that logp = o(n^1/3). We also provide a data‐driven method for choosing the penalty level that must be specified in obtaining Lasso and post‐Lasso estimates and establish its asymptotic validity under non‐Gaussian, heteroscedastic disturbances.     

Social Amplification of Wildfire Risk: The Role of Social Interactions and Information Sources

Wildfire is a persistent and growing threat across much of the western United States. Understanding how people living in fire‐prone areas perceive this threat is essential to the design of effective risk management policies. Drawing on the social amplification of risk framework, we develop a conceptual model of wildfire risk perceptions that incorporates the social processes that likely shape how individuals in fire‐prone areas come to understand this risk, highlighting the role of information sources and social interactions. We classify information sources as expert or nonexpert, and group social interactions according to two dimensions: formal versus informal, and generic versus fire‐specific. Using survey data from two Colorado counties, we empirically examine how information sources and social interactions relate to the perceived probability and perceived consequences of a wildfire. Our results suggest that social amplification processes play a role in shaping how individuals in this area perceive wildfire risk. A key finding is that both “vertical” (i.e., expert information sources and formal social interactions) and “horizontal” (i.e., nonexpert information and informal interactions) interactions are associated with perceived risk of experiencing a wildfire. We also find evidence of perceived “risk interdependency”—that is, homeowners’ perceptions of risk are higher when vegetation on neighboring properties is perceived to be dense. Incorporating social amplification processes into community‐based wildfire education programs and evaluating these programs’ effectiveness constitutes an area for future inquiry.     

STAR与ANN模型：证券价格非线性动态特征及可预测性研究

证券价格的可预测性一直是现代金融学的研究焦点。近年来,以平滑迁移(STAR)模型和神经网络(ANN)模型为代表,国外学者将许多非线性模型应用于证券价格非线性动态特征及可预测性的研究。本文采用多种线性、非线性股价预测模型对上证180指数短期和中长期可预测性进行研究,并基于统计指标和投资策略指标比较了不同模型的预测能力。结果表明:我国证券价格具有非线性特征,在短期和中长期水平上具有一定的可预测性,这对有效市场假说提出了质疑;ANN模型的预测能力多数情况下优于RW模型、线性AR模型和STAR模型,基于ANN模型的"盯市"投资策略能获得比"买入持有"投资策略更高的平均净收益。     

The Relational Antecedents of Interpersonal Helping: ‘Quantity’, ‘Quality’ or Both?

Having a large network of colleagues means having several opportunities to help those colleagues, as well as a higher chance of receiving requests for help from them. Employees with large networks are therefore expected to help more in the workplace than those with small networks. However, large networks are also associated with cognitive costs, which may reduce the focal employee's ability to both recognize the need for help and engage in helping behaviours. For these reasons, the authors assert an inverted U‐shaped relation between the size of an ego's social network and engagement in helping behaviour. However, high‐quality relationships imply higher mutual understanding between the actors, and hence lower cognitive costs. In turn, the position (and threshold) of the curve between network size and interpersonal helping should be influenced by the quality of the relationship between the provider and the beneficiaries of help. Analysis of employee‐level, single‐firm data supports these ideas, providing preliminary evidence that quality of relationship compensates for the difficulties that may arise from having large social networks.     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.     

Consistent Probabilistic Social Choice

Two fundamental axioms in social choice theory are consistency with respect to a variable electorate and consistency with respect to components of similar alternatives. In the context of traditional non‐probabilistic social choice, these axioms are incompatible with each other. We show that in the context of probabilistic social choice, these axioms uniquely characterize a function proposed by Fishburn (1984). Fishburn's function returns so‐called maximal lotteries, that is, lotteries that correspond to optimal mixed strategies in the symmetric zero‐sum game induced by the pairwise majority margins. Maximal lotteries are guaranteed to exist due to von Neumann's Minimax Theorem, are almost always unique, and can be efficiently computed using linear programming.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

‘Belonging’ to a Virtual Research Centre: Exploring the Influence of Social Capital Formation Processes on Member Identification in a Virtual Organization

Identification, a person's sense of belonging, is thought to have positive outcomes because those who identify strongly will be more motivated to achieve organizational goals. This study applies a social capital lens in order to highlight how different social capital formation processes contribute to member identification, with a focus on virtual organizations where dispersed membership may preclude face‐to‐face interactions. The study provides a rare opportunity to explore variation in identification in a single organizational context. The results highlight how some dimensions of social capital augment each other and affect identification through the four conditions that influence social capital development: time, interdependence, interaction and closure. The study suggests that, while creating interdependence and an array of means for interaction support the development of both social capital and identification, not all members of the organization need to feel equally connected. Partial closure appears beneficial for information flows and ‘renewal’ of the virtual organization.     

Credential Effects and the Returns to Education in China

Using the China Household Income Project (CHIP) data for 1995 and 2002, we examine the returns to education in China, separating out credential effects from pure years‐of‐schooling effects. The results are broadly consistent with the implications of China moving towards a market‐oriented economy: increasing returns to education where both years of schooling and credentials from completing key phases are rewarded; a decline in the importance of credentials as firms have more discretion to select the best‐suited employees irrespective of their credentials; more emphasis on credentials in the state sector; less emphasis on credentials for long‐tenured employees for whom the employer has more opportunity to assess productivity without relying on credentials; and a greater importance of credentials for females for whom the value of such signals may be more important.     

RITUALS OR GOOD WORKS: SOCIAL SIGNALING IN RELIGIOUS ORGANIZATIONS

We develop a model of social signaling of religiosity and cooperative behavior in religious organizations. The model embeds a ritual‐based religious organization in which signaling arises through the use of costly rituals, and a discipline‐based religious organization in which such signaling occurs through the monitoring of past behavior. We use this framework to contrast—positively and normatively—these two forms of social signaling. We show that ritual‐based religions, while using a costly and wasteful signal, also imply a higher level of coordination of behavior in social interactions and a higher incidence of mutual cooperation. Our welfare analysis suggests that communities are more likely to support a switch to a discipline‐based religion if strategic complementarities are high, and if there is sufficiently high level of public information about social behavior. This accords with the success of Calvin's Reformation in Switzerland and France, a process characterized by the reduction of rituals along with the creation of institutions to monitor and publicize individuals' behavior, such as the Consistory.     

Hybrid and Size‐Corrected Subsampling Methods

This paper considers inference in a broad class of nonregular models. The models considered are nonregular in the sense that standard test statistics have asymptotic distributions that are discontinuous in some parameters. It is shown in Andrews and Guggenberger (2009a) that standard fixed critical value, subsampling, and m out of n bootstrap methods often have incorrect asymptotic size in such models. This paper introduces general methods of constructing tests and confidence intervals that have correct asymptotic size. In particular, we consider a hybrid subsampling/fixed‐critical‐value method and size‐correction methods. The paper discusses two examples in detail. They are (i) confidence intervals in an autoregressive model with a root that may be close to unity and conditional heteroskedasticity of unknown form and (ii) tests and confidence intervals based on a post‐conservative model selection estimator.     

Wavelet‐Based Testing for Serial Correlation of Unknown Form in Panel Models

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet‐based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one‐way or two‐way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data‐driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data‐driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.