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The purpose of this paper is to design a game theory based model that not only provide theoretical basis to control brand counterfeiting, but it also provide a mechanism to enhance brand revenue.

This study used the dynamic game theory of incomplete information to understand and encounter the brand counterfeiting issues. The study analyzed the economic relationship of legitimate brand, counterfeiter and consumers using mixed strategy of the dynamic game theory of incomplete information.

The results have indicated that brands those take countermeasures to reduce counterfeiting earn maximum revenue, even when the legitimate brands and consumers are unaware from the actions of counterfeiting firms, the legitimate brands should take countermeasures to reduce counterfeiting to earn maximum revenue, and there exists optimal anti-counterfeiting cost for the legitimate brand. Further, this study provides theoretical basis where brand managers can decide to adopt or not to adopt anti-counterfeiting strategy, and also indicate the consequences of each decision.

Based upon the findings, the study put forward valuable managerial implications. The study revealed that the legitimate brand must emphasize on the significance of taking countermeasures against counterfeiter and also, brand managers should focus on making product traceable, empowering the consumer and ensuring coordination with government officials to control the counterfeiting issues.

This paper incorporates the role of legitimate brand, counterfeiter and a consumer to establish the dynamic game theory model using mixed strategy to understand and address the counterfeiting issues in the global market.

The purpose of this paper is to propose a portfolio procurement framework to response to uncertain customer demand and purchasing price volatility in a simultaneous manner. Then it aims to obtain optimal procurement and production decisions under the portfolio framework to maximize profit.

The portfolio procurement problem is modeled as a dynamic Stackelberg game and Nash equilibrium solutions are obtained. The portfolio procurement framework is analyzed in the settings, with both risk-neutral objective and downside risk constraints measure of contract prices.

By obtaining the Nash equilibrium solutions for both the buyer’s ordering decisions and the supplier’s optimum production decisions, Stackelberg game model for portfolio procurement is proved to be feasible. Additionally, downside risk constrains are proposed to help supply chain participants’ to evaluate the profitability and risk probabilities of the designed procurement contracts under the uncertain customer demand and spot market.

This paper assumes the supplier is risk averse and the buyer is risk neutral, and it would be interesting to examine the performances of portfolio procurement strategy with different risk attitudes participants.

This research could help the buyer respond to not only demand uncertainty but also the volatile spot price in the procurement process. Related optimal portfolio procurement strategy can be carried out to improve the enterprise’ procurement plan by adjusting the order of long-term contract, option contract and the spot market. The proposed framework could also help suppliers design and evaluate contracts for buyers with different risk preference, and on the other hand help the buyers decide if she should accept the contracts from the supplier.

This research should also increase awareness in both academia and industry on the opportunities of using the dynamic portfolio procurement approach to enhance flexibility and to mitigate the inventory as well as price risks in the procurement process. Effective downside risk constrains on contract prices could also help to protect the bottom line of companies with different risk preference.

The portfolio procurement framework proposed in this research can mitigate inventory and price risks simultaneously. Also, instead of solving the portfolio procurement planning problem in computational simulation experiments as in previous research, this paper proposed a dynamic game model for this portfolio-based procurement problem and obtained its Nash equilibrium solutions for both the buyer’s ordering decisions and the supplier’s optimum production decisions. Finally, an innovative and simple downside risk constraints has been designed to help the buyer evaluate supplier’s contract prices according to their individual risk preference.

This study aims to disclose how the nature of corporate ownership, stock efficiency and wage level affect the optimal proportion of employee stock.

This paper studies three duopoly markets: two private enterprises, two state-owned enterprises (SOEs) and a private enterprise and an SOE. The competitions between the two parties are taken as a two-stage dynamic sequential game and studied through back-induction.

The results reveal that the enterprise ownership has a directly bearing on the optimal proportion of employee stock and determines whether to implement the employee stock ownership plan (ESOP) and the specific level of the plan. The optimal proportion of employee stock is positively correlated with its contribution to enterprise efficiency. There are many influencing factors on the effect of wage level on the optimal proportion of employee stock, namely, the ownership nature of ESOP implementer and efficiency difference of different nature stocks.

The results of this study provide policy recommendations for companies preparing to implement ESOP.

The research findings provide policy implications for enterprises to prepare a suitable ESOP and the reform of national equities, especially the mixed-ownership reform in China.

The purpose of this paper is to study the problem of optimal Ramsey taxation in a finite-planning-horizon, representative-agent endogenous growth model including government expenditures as a productive input in capital formation and also with hidden actions.

Technically, Malliavin calculus and forward integrals are naturally introduced into the macroeconomic theory when economic agents are faced with different information structures arising from a non-Markovian environment.

The major result shows that the well-known Judd-Chamley Theorem holds almost surely if the depreciation rate is strictly positive, otherwise Judd-Chamley Theorem only holds for a knife-edge case or on a Lebesgue measure-zero set when the physical capital is completely sustainable.

The author believes that the approach developed as well as the major result established is new and relevant.

Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik’s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

The purpose of this paper is to summarize ways in which game theory has been or could be utilized within the humanitarian sector and to identify future research opportunities in this field. Game theory is a tool for modeling systems in which multiple decision makers act according to their own objectives and where individual choices affect system outcomes. Humanitarian logistics systems are often characterized by the presence of many such decision makers.

The authors synthesize literature from operations research, humanitarian logistics, and the political and management sciences that describes either game theory models or settings in which game theory has the potential to contribute practical insights. Papers are categorized based on the types of decision makers highlighted and the facets of humanitarian operations described.

The review of literature indicates that applications of game theory to humanitarian supply chain systems are limited to date but that many components of humanitarian operations can benefit from such study.

The literature on game theory in humanitarian supply chains is still emerging. The findings imply opportunities for researchers to advance both theoretical and practical understanding in this area.

Current research provides some managerial insights to humanitarian practitioners, but future practice-driven research could increase the impact of game theory models in the humanitarian sector.

This work reviews past research contributions in the area of game theory and humanitarian operations and recommends areas for future research.

In this article, we consider the nonparametric identification of Markov dynamic games models in which each firm has its own unobserved state variable, which is persistent over time. This class of models includes most models in the Ericson and Pakes (1995) and Pakes and McGuire (1994) framework. We provide conditions under which the joint Markov equilibrium process of the firms’ observed and unobserved variables can be nonparametrically identified from data. For stationary continuous action games, we show that only three observations of the observed component are required to identify the equilibrium Markov process of the dynamic game. When agents’ choice variables are discrete, but the unobserved state variables are continuous, four observations are required.

This experiment analyzes multi-offer versions of the signaling and screening litigation games, as well as a bilateral multi-offer litigation game. A plaintiff has either a low or a high claim on an uninformed defendant, and the two negotiate in an attempt to reach a pre-trial settlement. Trial is costly, and settlement generates surplus over which the two parties can bargain. In the signaling game, the defendant has the power to make the offer, while the plaintiff makes the offer in the screening game. Previous experiments on single-offer games find that disputes occur even when offers contain surplus not predicted under the theory, and fairness appears to be important in explaining deviations from theory. This research examines whether renegotiation in the form of successive sequential offers can yield efficiency gains via lower dispute rates. There are four main findings. One, under the one-sided multi-offer structure the excess dispute rate is 23 percentage-points lower in the screening game, and the high-offer dispute rate is 31 percentage-points lower in signaling game. The bilateral game yields an additional 15 percentage-point reduction in the high-offer dispute rate, but excess disputes persist. Two, in these games, proposers take advantage of the multi-offer opportunity and make around three to four offers per negotiation. Three, across games the surplus in a fair offer remains constant at about one-sixth of the surplus, but the empirical benchmark from which this is measured varies according to which player has the power to make the offer. In the one-sided games, the benchmark is the respective zero-surplus endpoint, but in the bilateral game the benchmark is the surplus midpoint. Fourth, dynamic behavior plays an important but complex role in observed outcomes. Multi-offer mechanisms may be alternatives to costly information transmission mechanisms like disclosure or discovery.

This paper explores the role of neural networks for decision making in dynamic environments which are characterized by risks and uncertainties, and also provides experimental evidence from a simulated data. Theoretical support is derived from theories of affective balance, and self‐organized criticality. The simulation is conducted for a two‐person‐constant sum game. The findings of the experiment are helpful in extending to managerial decision making which involves varying degrees of uncertainties. Such decisions are affected by forces both internal and external to the company, and making judgments in such a fuzzy future is highly probabilistic. It is suggested, therefore that neural networks are better able to capture the interactive dynamics of variables operating in a managerial decision environment. In sum, the findings indicate that decisions in general and business decisions in particular can greatly benefit from the parallel computational capabilities of neural networks.