Challenge Versus Exchange in Collective Decision Making: A Comparison of Two Simulation Models Based on Simulated Data

In collective decision making, actors can use different influence strategies to get their way. Differences in influence strategies may, or may not, be connected to differences in collective outcomes. This research studies two influence strategies: the exchange strategy and the challenge strategy. In the existing literature, these strategies are analyzed and compared using simulation models in which actor behavior regarding influence attempts based on one of the strategies is modeled explicitly. Until now, these models have been tested only empirically on limited data sets. However, a theoretical test is necessary to gain more precise insights in the effect of characteristics of collective decision making situations on the collective outcomes. In the present research, computer simulations are used in a structured comparison of two competing models (the iterative exchange model and challenge model). The analyses show that the outcomes of both models are captured for a large part in the actor characteristics on the issues. Besides this, the expected directions of challenges and exchanges play a major part in explaining the outcomes of the models. This research shows that the use of simulated data allows a structured search of the input space, which led to new insights into the iterative exchange model and challenge model, and therefore in the exchange strategy and the challenge strategy.     

Shield versus sword resource distribution in K-round duels

The paper considers optimal resource distribution between offense and defense in a duel. In each round of the duel two actors exchange attacks distributing the offense resources equally across K rounds. The offense resources are expendable (e.g. missiles), whereas the defense resources are not expendable (e.g. bunkers). The outcomes of each round are determined by a contest success functions which depend on the offensive and defensive resources. The game ends when at least one target is destroyed or after K rounds. We show that when each actor maximizes its own survivability, then both actors allocate all their resources defensively. Conversely, when each actor minimizes the survivability of the other actor, then both actors allocate all their resources offensively. We then consider two cases of battle for a single target in which one of the actors minimizes the survivability of its counterpart whereas the counterpart maximizes its own survivability. It is shown that in these two cases the minmax survivabilities of the two actors are the same, and the sum of their resource fractions allocated to offense is equal to 1. However, their resource distributions are different. In the symmetric situation when the actors are equally resourceful and the two contest intensities are equal, then the actor that fights for the destruction of its counterpart allocates more resources to offense. We demonstrate a methodology of game analysis by illustrating how the resources, contest intensities and number of rounds in the duels impact the survivabilities and resource distributions.     

Mathematical model and method of searching for a local extremum for the non-convex oriented polygons allocation problem

This work deals with the problem of optimal allocation of objects of a so-called irregular form. The objects are allocated on a strip of given width and with defects. This problem is insufficiently studied, but it is typical for many industries and is also interesting for developing the theory of solving cutting and packing problems. An analytical model of the problem using only continuous variables is written in terms of classical mathematical programming, and it is constructed on the basis of the original theory of φ-functions and structures of linear inequalities. The presented theory allows one to easily describe the conditions of mutual non-overlapping of objects and their allocation in the stock region. The exact method for searching a local minimum of the problem from any feasible initial point is based on the application of the active set strategy ideas. A number of examples of solving practical problems are considered.     

On optimal management of resources in distributed networks

In a network of processors, a distributed operating system must handle the management of shared resources. In this paper, it is shown how to solve this problem in using the model previously introduced in [1]. This model (interconnection of N Markov chains each representing locally a distributed process) allows us to prove the good functioning properties for some distributed problems such as the mutual exclusion problem and the deadlock problem, We also prove that fairness is a basic notion for setting the model’s parameters and obtain an optimal working of the network.     

Optimization and mechanism design

Mechanism design is about optimizing the allocation of resources when the parameters needed to determine an optimal allocation are privately held by the agents who will consume the resources. An agent’s report of her information will influence the resulting allocation which in turn will affect her utility. In such a situation, how can one simultaneously elicit the information that is privately held and choose the optimal allocation? This paper illustrates how standard results in linear programming play a role in the analysis of mechanism design problems. It is not a comprehensive survey. Rather, it focuses on two variations of a particular problem: the allocation of a single object.     

The virtual design team: A computational model of project organizations

Large scale and multidisciplinary engineering projects (e.g., design of a hospital building) are often complex. They usually involve many interdependent activities and require intensive coordination among actors (i.e., designers) to deal with activity interdependencies. To make such projects more effective and efficient, one needs to understand how coordination requirements are generated and what coordination mechanisms should be applied for given project situations. Our research on the Virtual Design Team (VDT) attempts to develop a computational model of project organizations to analyze how activity interdependencies raise coordination needs and how organization design and communication tools change team coordination capacity and project performance. The VDT model is built based on contingency theory (Galbraith, 1977) and our observations about collaborative and multidisciplinary work in large, complex projects. VDT explicitly models actors, activities, communication tools and organizations. Based on our extended information-processing view of organizations, VDT simulates the actions of, and interactions among actors as processes of attention allocation, capacity allocation, and communication. VDT evaluates organization performance by measuring emergent project duration, direct cost, and coordination quality. The VDT model has been tested internally, and evaluated externally through case-studies. We found three way qualitative consistency among predictions of the simulation model, of organization theory, and of experienced project managers. In this paper, we present the VDT model in detail and discuss some general issues involved in computational organization modeling, including level of abstraction of tasks and actors' reasoning, and model validation.     

基于D-S证据理论的水资源合理配置方案综合评价

由于水资源合理配置具有多目标性、模糊性和不确定性的特点,其方案的综合评价可作为一个数据融合问题.利用改进的D-S证据理论合成公式,并考虑不同指标之间权重,给出了水资源合理配置方案综合评价模型.石羊河流域水资源配置方案评价的实例研究表明,D-S证据理论在水资源合理配置方案评价中有较好的实用性.     

Optimal response against bioterror attack on airport terminal

We consider a potential bioterror attack on an airport. After the attack is identified, the government is faced with the problem of how to allocate limited emergency resources (human resources, vaccines, etc.) efficiently. The government is assumed to make a one-time resource allocation decision. The optimal allocation problem is discussed and it is shown how available information on the number of infected passengers can be incorporated into the model. Estimation for parameters of the cost function (number of deaths after the epidemic is over) is provided based on known epidemic models. The models proposed in the paper are demonstrated with a case study using real airport data.     

Lewis’ Modal Realism and Absence Causation

A major criticism of David Lewis’ counterfactual theory of causation is that it allows too many things to count as causes, especially since Lewis allows, in addition to events, absences to be causes as well. Peter Menzies has advanced this concern under the title “the problem of profligate causation.” In this paper, I argue that the problem of profligate causation provides resources for exposing a tension between Lewis’ acceptance of absence causation and his modal realism. The result is a different problem of profligate causation—one that attacks the internal consistency of Lewisian metaphysics rather than employing common sense judgments or intuitions that conflict with Lewis’ extensive list of causes.     

Strategic control and interests,its effects on decision outcomes

Application of the model to artificial data shows that actors with strong preferences in the center have more possibilities to realize good outcomes than other actors. On the basis of an empirical application it is shown that a Nash equilibrium does not always arise after a large number of iterations unless actors have learning capabilities or are severely restricted in their strategic behavior.

In political systems and large organizations, ultimate decision makers are usually just a small subset of all actors in the social system. To arrive at acceptable decisions, decision makers have to take into account the preferences of other actors in the system. Typically preferences of more interested and more powerful actors are weighted heavier than those of less interested and powerful actors. This implies that the total leverage of an actor on the decision is determined by the combination of his power (his potential) and his interest (his willingness to mobilize his power). As the exact level of an actor's leverage is difficult to estimate for the other actors in the system, an actor is able to optimize his effects on outcomes of decisions by providing strategic informatioa

In this paper, first an analytic solution is presented for the optimization of strategic leverage in collective decision making by one single actor. In this solution, the actor makes assumptions about the leverage other actors will show in decision making. Subsequently, the actor optimizes the outcomes of decisions by manipulating the distribution of his leverage over a set of issues.

The analytic solution can be theoretically interpreted by decomposing the solution into three terms, the expected external leverage of the other actors on the issue, the evaluation of the deviance of the expected from the preferred outcome of the issue, and the restrictions on the distribution of leverage over the issues. The higher the expectation of the leverages the other actors will allocate to the issue, the less an actor is inclined to allocate leverage to the issue. The higher the evaluation of the deviance, the more an actor is inclined to allocate leverage to the issue. This is restricted, however, by the required distribution of leverages over the issues. The researcher is able to manipulate these restrictions to investigate its consequences for the outcomes.

In the next step, we investigate whether we can find a Nash equilibrium if all actors optimize their leverage simultaneously. Under certain conditions, a Nash equilibrium can be found by an iterative process in which actors update their estimates oh each other's leverages on the basis of what the other actors have shown in previous iterations.     

Fluid dynamics models and their applications in transportation and pricing

Fluid dynamics models provide a powerful deterministic technique to approximate stochasticity in a variety of application areas. In this paper, we study two classes of fluid models, investigate their relationship as well as some of their applications. This analysis allows us to provide analytical models of travel times as they arise in dynamically evolving environments, such as transportation networks as well as supply chains. In particular, using the laws of hydrodynamic theory, we first propose and examine a general second-order fluid model. We consider a first-order approximation of this model and show how it is helpful in analyzing the dynamic traffic equilibrium problem. Furthermore, we present an alternate class of fluid models that are traditionally used in the context of dynamic traffic assignment. By interpreting travel times as price/inventory–sojourn-time relationships, we are also able to connect this approach with a tractable fluid model in the context of dynamic pricing and inventory management.     

影子价格及其在资源配置中的应用研究

为了应用影子价格实现资源在全社会的最优配置,本文通过线性规划的对偶理论和非线性优化问题的Kuhn-Tucker条件揭示了影子价格的本质,在资源配置优化问题中线性规划模型中的影子价格就是其对偶问题的最优解,非线性规划模型中的影子价格就是与最优解相对应的拉格朗日乘数。根据松紧定理解释了资源影子价格与资源限量之间的关系,还对线性规划模型与非线性规划模型中影子价格的不同表现进行了分析。最后阐明了影子价格在资源配置中的应用。     

Decentralization of responsibility for site decontamination projects: A budget allocation approach

A major problem currently confronting central governments is how to optimally allocate resources for decontamination of polluted sites. ‘Optimally’ here refers to obtaining maximum environmental benefits with the limited resources available. An important issue in budget allocation is that of decentralization, given the magnitude of the information flows between regional and central level necessary in a fully centralized approach. This paper investigates the use of mathematical programming models to support allocation procedures to obtain maximum environmental effectiveness and economic efficiency. We consider the situation where regional authorities provide limited, summary information to the central government, which then allocates budgets. The central government aims to maximize total environmental benefits, subject to a central budget constraint (and constraints on other resources). The problem can be formulated as a mixed integer programming problem, but the size of the problem precludes the search for optimal solutions. We present an effective heuristic and include computational results on its performance.     

Managing variances in manufacturing system design

The goal of this paper is to investigate how uncertainties in demand and production should be incorporated into manufacturing system design problems. We examine two problems in manufacturing system design: the resource allocation problem and the product grouping problem. In the resource allocation problem, we consider the issue of how to cope with uncertainties when we utilize two types of resources: actual processing capacity and stored capacity (inventory). A closed form solution of the optimal allocation scheme for each type of capacity is developed, and its performance is compared to that of the conventional scheme where capacity allocation and inventory control decisions are made sequentially. In the product grouping problem, we consider the issue of how we design production lines when each line is dedicated to a certain set of products. We formulate a mathematical program in which we simultaneously determine the number of production lines and the composition of each line. Two heuristics are developed for the problem.     

Arrangement Increasing Resource Allocation

Consider a system composed of several units. The performance of each unit can be affected by providing a portion of a limited amount of costly resources available. An allocation of resources to a unit results in a unit’s response that depends on the level of resources allocated to it and some of its random parameters. In this paper we consider cases where each unit has one or two random parameters. The overall performance of the system is mapped by a function on the vector of responses generated by all the units in the system. Our interest is in identifying the conditions on the response function of the units, the system performance function and the random parameters under which the random system performance as a function of the resource allocation has stochastic arrangement increasing property. This allows one to substantially reduce the number of allocation that needs to be searched to identify an optimal allocation that maximizes the expected utility derived from the system response as a result of the resource allocation.     

Modelling a multi-objective allocation problem in a government sponsored entrepreneur development programme

Typically the vendor allocation problem for a commercial company requires the buyer to allocate business between the selected vendor suppliers to meet its needs at minimum cost, taking account of its quality and delivery performance requirements. However in a government sponsored entrepreneur development programme, the allocation of business cannot only be made dependent on criteria that reflect the current ability of the vendor nor solely on criteria fulfilling the current needs of the buyer. It must also consider how it may aid the vendors to improve their business performance in the future and also on how it affects the development of that indigenous industry sector. Furthermore, conditions change from one year to the next so the problem is dynamic over time.This paper describes how a combination of Goal Programming model (GP) with pre-emptive priority ranking of goal constraints with a linear programming model facilitates allocation of businesses to entrepreneurs. In particular, the subject used in this paper is a Malaysia Government sponsored entrepreneur development programme for furniture maker, which administered by a commercial company. An innovative aspect of the work is the use of GP as a modelling rather than a solution technique. It is used to convert the conflicting multi-objectives that were expressed only in general policy terms to operational terms on which business could be allocated. The paper shows how it can be used to try to understand, and then formally model how managers use their judgement and experience in a complex multi-criteria situation. The paper goes on to show that using the formal model leads to more consistency in decision-making and an improvement in the achievement of the objectives. This is important as several different managers are having to make independent decisions on subsets of the vendors.     

考虑外部效应与风险因素的多项目优化模型及应用

多项目的组合优化与资源分配优化,实质上是经济学中资源配置与利用问题.运用经济学与管理学的相关理论和定量研究方法,建立了外部效应估算模型、项目评价模型、风险估算模型、多项目组合优化模型和关于资源分配优化的两层决策模型.相关模型的应用实例研究,说明了该模型的有效性.     

Optimal allocation and backup of computer resources under asymmetric information and incentive incompatibility

This paper presents the optimal allocation and backup of computing resources in a multidivisional firm in the presence of asymmetric information and incentive incompatibility. A game-theoretic model is developed and transformed to a linear programming problem. The solution to this linear programming problem enables the corporate headquarters to design a resource allocation scheme such that the revelation principle prevails and all divisions tell the truth. To cope with the combinatorial explosion of complexity caused by the resource constraint, a greedy-type algorithm and an averaged version of the original linear programming problem are developed to provide the upper and lower bounds. The greedy-type algorithm generates exact solutions for a wide range of instances. The lower bounds coincide with the exact solutions for the cases where the computer resource is either scarce or abundant. The averaged-version resource allocation model with slight modifications solves the optimal computer backup capacity problem. It determines how much back up capacity the firm should purchase when the firm's computer breaks down.     

DEA models for resource reallocation and production input/output estimation

This paper discusses the “inverse” data envelopment analysis (DEA) problem with preference cone constraints. An inverse DEA model can be used for a decision making unit (DMU) to estimate its input/output levels when some or all of its input/output entities are revised, given its current DEA efficiency level. The extension of introducing additional preference cones to the previously developed inverse DEA model allows the decision makers to incorporate their preferences or important policies over inputs/outputs into the production analysis and resource allocation process. We provide the properties of the inverse DEA problem through a discussion of its related multi-objective and weighted sum single-objective programming problems. Numerical examples are presented to illustrate the application procedure of our extended inverse DEA model. In particular, we demonstrate how to apply the model to the case of a local home electrical appliance group company for its resource reallocation decisions.