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Cellular automata are used to study two-dimensional local hawk-dove games. Both Nash and Pareto optimal concepts are used. The systems have fixed point, cyclic and chaotic-like regimes. The results differ significantly from that of the normal one.

Prisoner's Dilemma games with two and three strategies are studied. The corresponding replicator equations, their steady states and their asymptotic stability are discussed. Local Prisoner's Dilemma games are studied using Pareto optimality. As in the case with Nash updating rule, the existence of tit for tat strategy is crucial to imply cooperation even in one dimension. Pareto updating implies less erratic behavior since the steady state configurations are mostly fixed points or at most 2-cycle. Finally, Prisoner's Dilemma game is simulated on small-world networks which are closer to real systems than regular lattices. There are no significant changes compared to the results of the regular lattice.

In this paper, we present a novel sequential sampling methodology for solving multi-objective optimization problems. Random sequential sampling is performed using the information from within the non-dominated solution set generated by the algorithm, while resampling is performed using the extreme points of the non-dominated solution set. The proposed approach has been benchmarked against well-known multi-objective optimization algorithms that exist in the literature through a series of problem instances. The proposed algorithm has been demonstrated to perform at least as good as the alternatives found in the literature in problems where the Pareto front presents convexity, nonconvexity, or discontinuity; while producing very promising results in problem instances where there is multi-modality or nonuniform distribution of the solutions along the Pareto front.

In this paper, we design a revenue share contract when a supplier is the leader and a retailer is the follower in a two-echelon supply chain. The innovation lies in dividing the supplier’s marketing strategies into two types: the profit oriented and the sales oriented to distinguish the different decision objectives from previous literatures assuming the supplier and retailer are both profit oriented. The paper uses the revenue share contract to reach supply chain coordination and Pareto optimality in the supply chain with the profit-oriented suppliers. However, the contract makes supply chain collaboration solutions and the Pareto improvement with the sales oriented supplier conditional. Numerical examples are given to illustrate these cases.

A technique is proposed to represent games in characteristic function form as games in normal form, enabling the former to exploit the concepts of solution of the latter. A new solution for games in characteristic function form is then introduced and some properties are found. A generalization of a result of von Neumann and Morgenstern is thus obtained.

We characterize the space of deterministic, dominant-strategy incentive compatible, individually rational, and Pareto-optimal combinatorial auctions where efficiency is not required. We examine a model with two players and k nonidentical items (2^k outcomes), multidimensional types, private values, non-negative prices, and quasilinear preferences for the players with one relaxation — the players are subject to publicly-known budget constraints. We show that if it is publicly known that the players value the bundles more than the smaller of their budgets then the studied space includes one type of mechanism: autocratic mechanisms (a form of dictatorship). Furthermore, we prove that there are families of autocratic mechanisms that uniquely fulfill the basic properties of deterministic, dominant-strategy incentive compatible, individually rational, and Pareto-optimal. Interestingly the above basic properties are a weaker requirement than it may initially appear, as the property of Pareto optimality in our model of budget-constrained players and non-negative prices do not coincide with welfare maximization, i.e., efficiency as such is a much weaker requirement.

Public–Private Partnership (PPP) approaches in provision of public infrastructure projects usually involve conflicts. A win–win situation would be the desired goal of such collaborations for both public and private parties. However, stakeholders’ behaviors might result in undesirable worse conditions. Identification and interpretation of the involved parties’ individualistic behaviors to PPP problems can be addressed by game theory where it describes the inclinations and interactions of different parties who are in search of satisfying their self-interest-based objectives rather than system-wide approaches. Outcomes predicted by game theory, which are based on individuality, often differ from those presented by conventional optimization methods and they are more realistic. This study mainly scrutinizes the applicability of game theory into PPP rail projects and conflict resolution. The paper also evaluates the dynamic structure of the PPP problems and highlights the importance of consideration of the game’s evolutionary nature while studying such problems.

There are inherently various uncertainties in practical engineering, and reliability-based design optimization (RBDO) and robust design optimization (RDO) are two well-established methodologies when considering the uncertainties. Naturally, reliability-based robust design optimization (RBRDO) is a methodology developed recently by combining RBDO and RDO, in which the tolerances of random design variables are always assumed as constants. However, the tolerance of random design variables is a key factor for the objective robustness and manufacturing cost, and the tolerance allocation is the core problem in mechanical manufacturing. Inspired by the cost–tolerance relationship in mechanical manufacturing, this paper presents an integrated framework to simultaneously find the optimal design variable and the corresponding tolerance in the multi-objective RBRDO, with the trade-off between objective robustness and manufacturing cost. The failure mechanism of the constraint handling strategy of the constrained reference vector-guided evolutionary algorithm (C-RVEA) is discussed to solve the multi-objective optimization formulation. Then the robust repair operator and reliability-based repair operator are proposed to transform unfeasible solutions to the feasible ones under reliability constraints. Numerical results reveal that the proposed repair algorithm is effective. By solving the integrated multi-objective optimization problem, the Pareto front with the compromised solutions between the objective robustness and manufacturing cost could be obtained, from which decision makers can select the satisfying solution conveniently according to the preferred requirements.

We study the properties of economic growth in a region that is driven by the activities of the so-called creative class. On the consumption side of our regional economy, we focus on an infinitely lived creative class household, and on the production side of this same economy, we concentrate on a final good that is produced using creative and physical capital. In this setting, we first define and then characterize a competitive equilibrium for our regional economy. Second, we show that this competitive equilibrium is Pareto optimal. Third, we demonstrate that sustained growth in this regional economy is impossible when the value of a key parameter of the production function is less than or equal to unity. Fourth, we specify the conditions in our model that need to hold for there to be sustained economic growth. Fifth, we study what happens to the share of physical capital in our region’s total income. Finally, we analyze what happens to the asymptotic growth rate of physical capital and consumption when a regional authority taxes the returns from physical capital.

Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements on the optimization methods used. In this paper, we point out some shortcomings of some widely used basic methods of multi-objective optimization. As an alternative, we suggest using interactive approaches where the role of a decision maker or a designer is emphasized. Interactive multi-objective optimization has been shown to suit well for chemical process design problems because it takes the preferences of the decision maker into account in an iterative manner that enables a focused search for the best Pareto optimal solution, that is, the best compromise between the conflicting objectives. For this reason, only those solutions that are of interest to the decision maker need to be generated making this kind of an approach computationally efficient. Besides, the decision maker does not have to compare many solutions at a time which makes interactive approaches more usable from the cognitive point of view. Furthermore, during the interactive solution process the decision maker can learn about the interrelationships among the objectives. In addition to describing the general philosophy of interactive approaches, we discuss the possibilities of interactive multi-objective optimization in chemical process design and give some examples of interactive methods to illustrate the ideas. Finally, we demonstrate the usefulness of interactive approaches in chemical process design by summarizing some reported studies related to, for example, paper making and sugar industries. Let us emphasize that the approaches described are appropriate for problems with more than two objective functions.

Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements on the optimization methods used. In this chapter, we point out some shortcomings of some widely used basic methods of multi-objective optimization. As an alternative, we suggest using interactive approaches where the role of a decision maker or a designer is emphasized. Interactive multi-objective optimization has been shown to suit well for chemical process design problems because it takes the preferences of the decision maker into account in an iterative manner that enables a focused search for the best Pareto optimal solution, that is, the best compromise between the conflicting objectives. For this reason, only those solutions that are of interest to the decision maker need to be generated making this kind of an approach computationally efficient. Besides, the decision maker does not have to compare many solutions at a time which makes interactive approaches more usable from the cognitive point of view. Furthermore, during the interactive solution process the decision maker can learn about the interrelationships among the objectives. In addition to describing the general philosophy of interactive approaches, we discuss the possibilities of interactive multi-objective optimization in chemical process design and give some examples of interactive methods to illustrate the ideas. Finally, we demonstrate the usefulness of interactive approaches in chemical process design by summarizing some reported studies related to, for example, paper making and sugar industries. Let us emphasize that the approaches described are appropriate for problems with more than two objective functions.