The Theory of Socially Embedded Games: Applications and Extensions to Open and Closed Games

An earlier article, drawing on the mathematical theory of rules and rule complexes, extends and generalizes game theory (GGT). The theory has been used to conceptualize and analyze diverse social relationships, roles, and games as particular types of rule complexes.For instance, a social role, as a major basis of a parent's action in a game, consists of at least four key components – which are mathematical objects – in the determination of action: value complex, model of reality (including beliefs and knowledge bases), a repertoire of acts, routines, programs, and strategies, and modality, a role-specific algorithm for determining or generating action in game settings. This article applies and extends GGT in analyses of a market bargaining game (a type of open game) and of the classical game of prisoners' dilemma (a type of closed game). The applications show the concrete effects of social embeddedness on game structuring, game interaction patterns and outcomes, and social equilibria.     

Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis

Improving productive efficiency is an increasingly important determinant of the future of the swine industry in Hawaii. This paper examines the productive efficiency of a sample of swine producers in Hawaii by estimating a stochastic frontier production function and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models. The technical efficiency estimates obtained from the two frontier techniques are compared. The scale properties are also examined under the two approaches. The industry's potential for increasing production through improved efficiency is also discussed.     

Assessment of Interjudge Reliability in the Open-Ended Questions Coding Process

In the process of coding open-ended questions, the evaluation of interjudge reliability is a critical issue. In this paper, using real data, the behavior of three coefficients of reliability among coders, Cohen’s K, Krippendorff’s α and Perreault and Leigh’s I _r are patterned, in terms of the number of judges involved and the categories of answer defined. The outcome underlines the importance of both variables in the valuations of interjudge reliability, as well as the higher adequacy of Perreault and Leigh’s I _r and Krippendorff’s α for marketing and opinion research.     

DEVIL'S ADVOCACY IN MANAGERIAL DECISION-MAKING

There is some debate about the potential value of using devil's advocates in top-level organizational decision-making. In this paper, the contrasting views on this question are summarized briefly and the field and laboratory research on the devil's advocate and related techniques is discussed. This research is then used as the basis for detailed suggestions on the effective use of devil's advocates in improving managerial decisions.     

Simpler Probabilistic Population Forecasts: Making Scenarios Work

The traditional high-low-medium scenario approach to quantifying uncertainty in population forecasts has been criticized as lacking probabilistic meaning and consistency. This paper shows, under certain assumptions, how appropriately calibrated scenarios can be used to approximate the uncertainty intervals on future population size and age structure obtained with fully stochastic forecasts. As many forecasting organizations already produce scenarios and because dealing with them is familiar territory, the methods presented here offer an attractive intermediate position between probabilistically inconsistent scenario analysis and fully stochastic forecasts.     

On minimum distance estimators for unimodal densities

It is shown that minimum distance estimators for families of unimodal densities are always consistent; the rate of convergence is indicated. An algorithm is proposed for computing the minimum distance estimator for the family of all unimodal densities. References are given to the maximum likelihood method and the kernel method.     

Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.     

A strategic human resource perspective of firm competitive behavior

Embedded within firms are unique stores of intangible human assets that likely influence the way firms compete. We argue that the human and social capital of a firm, particularly at the upper echelon and board of director (BOD) levels, contribute to the firm's awareness of the competitive environment and its motivation and ability to undertake numerous, complex, and forceful competitive actions. We also suggest that the firm's executive compensation systems moderate the effects of these intangible human assets on firm competitive behavior. By examining how human capital, intra-firm social capital, and executive compensation influence firm competitive behavior, we advance a strategic HRM perspective of firm competitive behavior and outline several implications for future research.     

Are generalized call-spreads efficient?

In order to explain coexistence of a deductible for low values of the loss and an upper limit for high values of the loss in insurance contracts, we consider the exchange of risk between two rank dependent expected utility maximizers. It is shown that if the insurer (insured) takes more into account the lowest outcomes – hence maximal losses – than the insured (insurer), then the optimal contract has an upper limit (includes a deductible for high values of the loss). If furthermore, the insured (insurer) neglects the highest outcomes while the insurer (insured) does not, the optimal contract includes a deductible (full insurance) for low values of the loss.     

Are robust estimation methods useful in the structural errors-in-variables model?

Summary In this paper it is investigated whether robust estimation procedures for the parameters of a regression model are also applicable when the observations are generated by the errors-in-variables model. Specifically, attention is paid to bounded-influence estimators, i.e. estimators that are constructed in such a way that the influence of a single observation on the outcome of the estimator is bounded. Both the classical errors-in-variables model and models with contaminated observational errors are considered.The authors are indebted to a referee for his valuable comments on an earlier version of this paper.