Changing behaviours in defence acquisition: a game theory approach

Why do so many major defence contracts fail to deliver to the contractually agreed performance, time and cost requirements? This paper identifies the conspiracy of optimism as an important factor in the initiation of many projects. Using a combination of Game Theory and participatory workshops, we formulate a theory on the conspiracy of optimism and test it experimentally. This work forms part of a culture and behaviour change initiative within Defence Acquisition involving the Ministry of Defence and many defence contractors.     

Analysis of regression in game theory approach

Working with multiple regression analysis a researcher usually wants to know a comparative importance of predictors in the model. However, the analysis can be made difficult because of multicollinearity among regressors, which produces biased coefficients and negative inputs to multiple determination from presum ably useful regressors. To solve this problem we apply a tool from the co‐operative games theory, the Shapley Value imputation. We demonstrate the theoretical and practical advantages of the Shapley Value and show that it provides consistent results in the presence of multicollinearity. Copyright © 2001 John Wiley & Sons, Ltd.     

The impact of intermediaries on a negotiation: an approach from game theory

Central European Journal of Operations Research - Standard approaches to model interaction networks are limited in their capacity to describe the nuances of real communication. We present a game...     

Cooperative game theory approach to allocating benefits of horizontal cooperation

Logistics costs in general, and transportation costs in particular, represent a large fraction of the operating costs of many companies. One way to try to reduce these costs is through horizontal cooperation among shippers. Thus, when the transportation needs of two or more companies are merged, their collective transportation requirements can be met at lower cost. The attainable cost savings are due to economies of scale, which translate into cheaper rates due to increased negotiation power, use of larger vehicles and bundling of shipments. In this paper, a linear model is presented and used to study the cost savings that different companies may achieve when they merge their transportation requirements. On the one hand, solving this optimization model for different collaboration scenarios allows testing and quantifying the synergies among different potential partners, thus identifying the most profitable collaboration opportunities. On the other, the problem of allocating the joint cost savings of the cooperation is tackled using cooperative game theory. The proposed approach is illustrated with an example in which different cooperative game solution concepts are compared. Extensive numerical experiments have also been carried out to gain insight into the properties of the corresponding cost savings game and the behavior of the different solution concepts.     

Coordination of supply chains by option contracts: A cooperative game theory approach

Manufacturer–retailer supply chains commonly adopt a wholesale price mechanism. This mechanism, however, has often led manufacturers and retailers to situations of conflicts of interest. For example, due to uncertain market demand, retailers prefer to order flexibly from manufacturers so as to avoid incurring inventory costs and to be able to respond flexibly to market changes. Manufacturers, on the other hand, prefer retailers to place full orders as early as possible so that they can hedge against the risks of over- and under-production. Such conflicts between retailers and manufacturers can result in an inefficient supply chain. Motivated by this problem, we take a cooperative game approach in this paper to consider the coordination issue in a manufacturer–retailer supply chain using option contracts. Using the wholesale price mechanism as a benchmark, we develop an option contract model. Our study demonstrates that, compared with the benchmark based on the wholesale price mechanism, option contracts can coordinate the supply chain and achieve Pareto-improvement. We also discuss scenarios in which option contracts are selected according to individual supply chain members’ risk preferences and negotiating powers.     

Social status and long-run effects of monetary policy in a two-sector monetary economy of endogenous growth

We develop a two-sector monetary economy with human capital accumulation and a cash constraint applied to both consumption and investment to examine the ways in which social status affects the impact of monetary policy on the long-run economic growth rate. Our findings suggest that the formation of human capital is an important determinant to the super-neutrality of money in the growth-rate sense. Within an economy with Lucas-type human capital formation, money is super-neutral; however, within an economy where human capital accumulation formation is more generalized, and in which both physical and human capital are used as inputs, the growth rate in money will have a negative effect on the long-run growth rate of the economy. The existence, uniqueness and saddle-path stability of balanced-growth equilibrium are also examined.     

Corruption and botnet defense: a mean field game approach

Recently developed toy models for the mean-field games of corruption and botnet defence in cyber-security with three or four states of agents are extended to a more general mean-field-game model with 2d states, \(d\in \mathbf {N}\). In order to tackle new technical difficulties arising from a larger state-space we introduce new asymptotic regimes, namely small discount and small interaction asymptotics. Moreover, the link between stationary and time-dependent solutions is established rigorously leading to a performance of the turnpike theory in a mean-field-game setting.     

OR as rational choice: a decision and game theory perspective

Social science disciplines have used decision theory and game theory to provide metaphorical understanding and analytical rigour in their particular domains. The paper explores whether a similar perspective can be applied to operational research (OR) in order to provide an integrating theme for both theory and practice. It is argued that, while the methods of OR are instrumentally rational, OR interventions embrace non-instrumental aspects as well. A case study of an application of decision theory is described and analysed from a decision and game theory (DGT) perspective. The case demonstrates that although the model developed was instrumental, the structure and content of the model reflected the normative and communicative aspects of the decision context. The paper concludes that OR could use a DGT perspective as a conceptual framework for the teaching, research and practice of OR.     

Fiscal policy lags and income adjustment processes

The interest in the impact of fiscal policy lags on economic stability increased in the last decade. Several studies have been made on delays either in the government expenditure or in the tax system, where lags exist between the accrual and the payment of taxes. Nevertheless there is in the literature no model where time delays in government expenditures and in tax revenues are considered together as it happens in the real world. In this paper we remedied this defect and proposed a macro-dynamic model where two delays appear: the first pertains to the public expenditure, the second, to the tax revenue. The resulting system of delayed differential equations is studied qualitatively and numerically. The analysis suggests that only particular combinations of the two delays make the system stable. Prevalently the system is unstable and chaotic motions may arise. This implies that the economy may need appropriate structural changes in the public sector to improve fiscal policy outcomes in such a way they may really be consistent with their stabilization purposes.     

Solution of a problem of game theory

We solve the probabilistic problem of detecting a random object in a multidimensional domain using the apparatus of game theory.Translated from Statisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 116–122, 1988.     

Interactions between government and firms: a differential game approach

In this paper we study an extended version of the model described in Gradus (J. Econ. 81:1092–1109, 1989) in order to determine the optimal taxation policy of a government and its effects on the stock of capital goods growth as a result of the activity developed by firms. It is shown that, by introducing a wealth tax, there exists an optimal wealth tax rate for which the open-loop/feedback Nash/Stackelberg equilibria coincide, maximizing the payments for both agents (government and firms), so that the open-loop Stackelberg equilibrium becomes both time consistent and subgame perfect. Partially supported by MEC (Spain) Grants MTM2006-13468, BMF2002-03493 and project ‘Ingenio Mathematica (i-MATH)’ No. CSD2006-00032 (Consolider-Ingenio 2010).     

A symbolic finite-state approach for automated proving of theorems in combinatorial game theory

We develop a finite-state automata approach, implemented in a Maple package Toads and Frogs available from our websites, for conjecturing, and then rigorously proving, values for large families of positions in Richard Guy's combinatorial game ‘Toads and Frogs’. In particular, we prove a conjecture of Jeff Erickson.     

Pollution control: A differential game approach

Transnational pollution is formulated as a differential game between two sovereign governments. The symmetric open loop Nash equilibrium is shown to yield more pollution than in a cooperative solution. A model of Stackelberg leadership in pollution control is also investigated. The possibility of limit cycles is illustrated, using bifurcation theory.     

Heterogeneous surface-to-air missile defense battery location: a game theoretic approach

In the context of an air defense missile-and-interceptor engagement, a challenge for the defender is that surface-to-air missile batteries often must be located to protect high-value targets dispersed over a vast area, subject to which an attacker may observe the disposition of batteries and subsequently develop and implement an attack plan. To model this scenario, we formulate a two-player, extensive form, three-stage, perfect information, zero-sum game that accounts for, respectively, a defender’s location of batteries, an attacker’s launch of missiles against targets, and a defender’s assignment of interceptor missiles from batteries to incoming attacker missiles. The resulting trilevel math programming formulation cannot be solved via direct optimization, and it is not suitable to solve via full enumeration for realistically-sized instances. We instead adapt the game tree search technique Double Oracle, within which we embed either of two alternative heuristics to solve an important subproblem for the attacker. We test and compare these solution methods to solve a designed set of 52 instances having parametric variations, from which we derive insights regarding the nature of the underlying problem. Enhancing the solution methods with alternative initialization strategies, our superlative methodology attains the optimal solution for over 75% of the instances tested and solutions within 3% of optimal, on average, for the remaining 25% of the instances, and it is promising for realistically-sized instances, scaling well with regard to computational effort.     

Evolutionary game theory on measure spaces: Well-posedness

An attempt is made to find a comprehensive mathematical framework in which to investigate the problems of well-posedness and asymptotic analysis for fully nonlinear evolutionary game theoretic models. The model should be rich enough to include all classical nonlinearities, e.g., Beverton–Holt or Ricker type. For several such models formulated on the space of integrable functions, it is known that as the variance of the payoff kernel becomes small the solution converges in the long term to a Dirac measure centered at the fittest strategy; thus the limit of the solution is not in the state space of integrable functions. Starting with the replicator–mutator equation and a generalized logistic equation as bases, a general model is formulated as a dynamical system on the state space of finite signed measures. Well-posedness is established, and then it is shown that by choosing appropriate payoff kernels this model includes all classical density models, both selection and mutation, and discrete and continuous strategy (trait) spaces.     

Complex adaptive systems and game theory: An unlikely union

A Complex Adaptive System is a collection of autonomous, heterogeneous agents, whose behavior is defined with a limited number of rules. A Game Theory is a mathematical construct that assumes a small number of rational players who have a limited number of actions or strategies available to them. The CAS method has the potential to alleviate some of the shortcomings of GT. On the other hand, CAS researchers are always looking for a realistic way to define interactions among agents. GT offers an attractive option for defining the rules of such interactions in a way that is both potentially consistent with observed real‐world behavior and subject to mathematical interpretation. This article reports on the results of an effort to build a CAS system that utilizes GT for determining the actions of individual agents. © 2009 Wiley Periodicals, Inc. Complexity, 16,24–42, 2010     

Controlled predatory pricing in a multiperiod Stackelberg game: an MPEC approach

We analyze a multiperiod oligopolistic market where each period is a Stackelberg game between a leader firm and multiple follower firms. The leader chooses his production level first, taking into account the reaction of the followers. Then, the follower firms decide their production levels after observing the leader’s decision. The difference between the proposed model and other models discussed in literature is that the leader firm has the power to force the follower firms out of business by preventing them from achieving a target sales level in a given time period. The leader firm has an incentive to lower the market prices possibly lower than the Stackelberg equilibrium in order to push the followers to sell less and eventually go out of business. Intentionally lowering the market prices to force competitors to fail is known as predatory pricing, and is illegal under antitrust laws since it negatively affects consumer welfare. In this work, we show that there exists a predatory pricing strategy where the market price is above the average cost and consumer welfare is preserved. We develop a mixed integer nonlinear problem (MINLP) that models the multiperiod Stackelberg game. The MINLP problem is transformed to a mixed integer linear problem (MILP) by using binary variables and piecewise linearization. A cutting plane algorithm is used to solve the resulting MILP. The results show that firms can engage in predatory pricing even if the average market price is forced to remain higher than the average cost. Furthermore, we show that in order to protect the consumers, antitrust laws can control predatory pricing by setting rules on consumer welfare.     

Optimal budgetary and monetary policies under uncertainty: A stochastic control approach

In this paper, we determine optimal budgetary and monetary policies for Austria using a small macroeconometric model. We use a Keynesian model of the Austrian economy, called FINPOL1, estimated by ordinary least squares, which relates the main objective variables of Austrian economic policies, such as the growth rate of real gross domestic product, the rate of unemployment, the rate of inflation, the balance of payments, and the ratio of the federal budget deficit to GDP, to fiscal and monetary policy instruments, namely expenditures and revenues of the federal budget and money supply. Optimal fiscal and monetary policies are calculated for the model under a quadratic objective function using the algorithm OPTCON for the optimum control of nonlinear stochastic dynamic systems. Several control experiments are performed in order to assess the influence of different kinds of uncertainty on optimal budgetary and monetary policies. Apart from deterministic optimization runs, different assumptions about parameter uncertainties are introduced; the results of these different stochastic optimum control experiments are compared and interpreted.