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.     

Analyzing Risky Choices: Q‐learning for Deal‐No‐Deal

In this paper, we derive an optimal strategy for the popular Deal or No Deal game show. To do this, we use Q‐learning methods, which quantify the continuation value inherent in sequential decision making in the game. We then analyze two contestants, Frank and Susanne, risky choices from the European version of the game. Given their choices and our optimal strategy, we find what their implied bounds would be on their levels of risk aversion. Previous empirical evidence in risky decision making has suggested that past outcomes affect future choices and that contestants have time‐varying risk aversion. We demonstrate that the strategies of Frank and Susanne are consistent with constant risk aversion levels except for their final risk‐seeking choice. We conclude with directions for future research. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling international investment decisions for financial holding companies

This research analyzes the internationalization process model developed by Johanson and Vahlne and derives two integer programming investment decision models that consider the risk attitudes of investment firms. Johanson and Vahlne’s model provides a starting point for building a model that suits the investment approach and decision making process of financial holding companies. In practice, when firms make an international investment decision, there is a need for a model that can generate outputs based on financial measures such as profit, investment returns, and tolerable levels of risk. Thus, in this paper, Johanson and Vahlne’s concepts are studied and financial managers are interviewed to derive models that match the investment decision procedures of the firms. The model helps firms manage the risks of their investments and derive accurate investment strategies based on investment objectives and constraints.     

Decision support models in climate policy

Climate change is considered among the most critical risks that global society faces in this century. So far, climate policy strategies have been evaluated by means of a variety of climate-economy models, or Integrated Assessment Models (IAMs), in the aim of supporting climate-related decision making. However, their inherent complexity, the number and nature of driving assumptions, and usual exclusion of stakeholders from the modelling process raise the issue of the extent to which they can provide fruitful insights for policy makers. Moreover, as with all modelling frameworks, IAMs inevitably fail to incorporate all relevant types of uncertainty and risk when used as stand-alone tools. This exclusion can have a significant impact on the model outcomes, but can be mitigated if experts’ knowledge is elicited in a structured manner and effectively taken into account, towards identifying such factors or reducing respective knowledge gaps. At the same time, a growing number of research publications have been suggesting decision support frameworks for assessing specific aspects in climate policy, based on “bottom-up” approaches and participatory processes. The objective of this paper is to provide a critical review of such frameworks—namely Portfolio Analysis, Multiple Criteria Decision Making and Fuzzy Cognitive Maps—in order to explore their strengths and weaknesses in this area, and propose a new integrative approach, appropriately exploiting blends of these frameworks, to productively complement IAMs, towards enhancing climate policy support.     

MOIRA: A decision support system for decision making on aquatic ecosystems contaminated by radioactive fallout

Interventions to restore radionuclide contaminated aquatic ecosystems may reduce individual and collective radiation doses, but may also result in detrimental ecological, social and economic effects. Decision makers must carefully evaluate possible impacts before choosing a countermeasure, hence decision analysis methods constitute an important aid to rank intervention strategies after the contamination of an aquatic ecosystem. We describe MOIRA, a decision support system for the identification of optimal remedial strategies to restore water systems after accidental introduction of radioactive substances. MOIRA includes an evaluation module based on a multi-attribute value model to rank alternatives and a module to perform multiparametric sensitivity analyses, both with respect to weights and values, to allow us to gain insights into the problem. The problem is under certainty since the validation of models used to quantify countermeasure impacts suggests little uncertainty in policy effects.The system is implemented in a PC based decision support system which allows the inclusion of all relevant information.     

Dynamic decision making for graphical models applied to oil exploration

We present a framework for sequential decision making in problems described by graphical models. The setting is given by dependent discrete random variables with associated costs or revenues. In our examples, the dependent variables are the potential outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to develop an optimal selection strategy of wells that incorporates a chosen utility function within an approximated dynamic programming scheme. We propose and compare different approximations, from naive and myopic heuristics to more complex look-ahead schemes, and we discuss their computational properties. We apply these strategies to oil exploration over multiple prospects modeled by a directed acyclic graph, and to a reservoir drilling decision problem modeled by a Markov random field. The results show that the suggested strategies clearly improve the naive or myopic constructions used in petroleum industry today. This is useful for decision makers planning petroleum exploration policies.     

Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems

Risk and return are interdependent in a stock portfolio. To achieve the anticipated return, comparative risk should be considered simultaneously. However, complex investment environments and dynamic change in decision making criteria complicate forecasts of risk and return for various investment objects. Additionally, investors often fail to maximize their profits because of improper capital allocation. Although stock investment involves multi-criteria decision making (MCDM), traditional MCDM theory has two shortfalls: first, it is inappropriate for decisions that evolve with a changing environment; second, weight assignments for various criteria are often oversimplified and inconsistent with actual human thinking processes.In 1965, Rechenberg proposed evolution strategies for solving optimization problems involving real number parameters and addressed several flaws in traditional algorithms, such as their use of point search only and their high probability of falling into optimal solution area. In 1992, Hillis introduced the co-evolutionary concept that the evolution of living creatures is interactive with their environments (multi-criteria) and constantly improves the survivability of their genes, which then expedites evolutionary computation. Therefore, this research aimed to solve multi-criteria decision making problems of stock trading investment by integrating evolutionary strategies into the co-evolutionary criteria evaluation model. Since co-evolution strategies are self-calibrating, criteria evaluation can be based on changes in time and environment. Such changes not only correspond with human decision making patterns (i.e., evaluation of dynamic changes in criteria), but also address the weaknesses of multi-criteria decision making (i.e., simplified assignment of weights for various criteria).Co-evolutionary evolution strategies can identify the optimal capital portfolio and can help investors maximize their returns by optimizing the preoperational allocation of limited capital. This experimental study compared general evolution strategies with artificial neural forecast model, and found that co-evolutionary evolution strategies outperform general evolution strategies and substantially outperform artificial neural forecast models. The co-evolutionary criteria evaluation model avoids the problem of oversimplified adaptive functions adopted by general algorithms and the problem of favoring weights but failing to adaptively adjust to environmental change, which is a major limitation of traditional multi-criteria decision making. Doing so allows adaptation of various criteria in response to changes in various capital allocation chromosomes. Capital allocation chromosomes in the proposed model also adapt to various criteria and evolve in ways that resemble thinking patterns.     

A simulation optimization method that considers uncertainty and multiple performance measures

Simulation optimization provides a structured approach to system design and configuration when analytical expressions for input/output relationships are unavailable. This research focuses on the development of a new simulation optimization technique applicable to systems having multiple performance measures. The aim of this research is to incorporate a simulation end user’s preference towards risk and uncertainty into the search process for the best decision alternative. Automation of the optimization procedure is a necessity. Therefore, this paper proposes a simulation optimization method that involves a preference model, specifically adapted for decision making with simulation models.     

Feature-based elimination: Model and empirical comparison

Experimental and verbal protocol research suggest that consumers appear to use noncompensatory screening strategies to remove alternatives and simplify complex choice situations prior to making a choice. Existing multi-phased choice models assume that the consumer initially evaluates each alternative to determine whether it should pass the first-stage screen and enter the choice set. The feature-based elimination model proposed in this study allows the consumer to avoid processing information for each alternative when forming the choice set. The consumer is assumed to apply a sequence of noncompensatory screens, similar to the elimination-by-aspects strategy, to form the choice set. An empirical application of the model demonstrates that cross-sectional heterogeneity in screening strategies can also be accommodated. One finding from this application is that heterogeneity in screening strategies may be at least as prevalent as heterogeneity in preferences. A comprehensive empirical comparison of the proposed model with existing two-stage models for scanner panel data shows that the model performs at least as well as all existing models and substantially better than most. The empirical performance of the model, coupled with its theoretical appeal and consistency with actual accounts of decision making in complex situations, make the proposed model an appealing alternative to existing multi-phased choice models.     

考虑炒作效应的网络视频平台商业模式选择

随着网络视频产业迅猛发展,越来越多的视频节目依赖炒作宣传吸引关注度获取丰厚的商业收益。本文考虑时间因素和炒作因素,基于用户需求不确定性建立连续时间型收益模型。应用哈密顿方法求解非线性优化问题,得到了炒作效应影响下的视频用户支付意愿变化规律,网络视频商业模式的动态演化趋势,并确立了最优的视频节目定价和营销炒作策略。研究结果表明,选择收费模式用户人数随着炒作时间的增加而增加,而选择免费模式用户人数随着炒作时间的增加而减少;随着炒作效应的提高,混合型免费—收费模式会逐渐演化为单一收费模式,且视频平台利润呈现倒“U”型变化趋势。研究结论揭示了营销炒作对视频定价、用户支付意愿和收视行为的影响,为视频平台更好的了解用户需求,制定商业策略提供决策依据。     

Decision maker preferences for information in complex decision making: New directions for OR intervention

Operations research models are used in many business and non-business entities to support a variety of decision making activities, primarily well-defined, operational decisions. This is due to the traditional emphasis of these models on optimal solutions to pre-specified problems. Some attempts have been made to use OR models in support of more complex, strategic decision making. Traditionally, these models have been developed without explicit consideration for the information processing abilities and limitations of the decision makers, who interact with, provide input to, and receive output from such models.Research in judgement and decision making show that human decisions are influenced by a number of factors including, but not limited to, information presentation modes; information content, modes, e.g., quantitative versus qualitative; order effects such as primacy, recency; and simultaneous versus sequential presentation of data.This article presents empirical research findings involving executive business decision makers and their preferences for information in decision making scenarios. These preference functions were evaluated using OR techniques. The results indicate that decision makers view information in different ways. Some decision makers prefer qualitative, narrative, social information, whereas other prefer quantitative, numerical, firm specific information. Results also show that decision making tasks influence the preference structure of decision makers, but that in general, the preference are relatively stable across tasks.The results imply that for OR models to be more useful in support of non-routine decision making, attention needs to be focused on the information content and presentation effects of model inputs and outputs.     

Iterative algorithms for improving consistency of intuitionistic preference relations

Consistency of preference relations is an important research topic in decision making with preference information. The existing research about consistency mainly focuses on multiplicative preference relations, fuzzy preference relations and linguistic preference relations. Intuitionistic preference relations, each of their elements is composed of a membership degree, a non-membership degree and a hesitation degree, can better reflect the very imprecision of preferences of decision makers. There has been little research on consistency of intuitionistic preference relations up to now, and thus, it is necessary to pay attention to this issue. In this paper, we first propose an approach to constructing the consistent (or approximate consistent) intuitionistic preference relation from any intuitionistic preference relation. Then we develop a convergent iterative algorithm to improve the consistency of an intuitionistic preference relation. Moreover, we investigate the consistency of intuitionistic preference relations in group decision making situations, and show that if all individual intuitionistic preference relations are consistent, then the collective intuitionistic preference relation is also consistent. Moreover, we develop a convergent iterative algorithm to improve the consistency of all individual intuitionistic preference relations. The practicability and effectiveness of the developed algorithms is verified through two examples.     

An approach to group decision making based on interval multiplicative and fuzzy preference relations by using projection

This paper presents a consensus model for group decision making with interval multiplicative and fuzzy preference relations based on two consensus criteria: (1) a consensus measure which indicates the agreement between experts’ preference relations and (2) a measure of proximity to find out how far the individual opinions are from the group opinion. These measures are calculated by using the relative projections of individual preference relations on the collective one, which are obtained by extending the relative projection of vectors. First, the weights of experts are determined by the relative projections of individual preference relations on the initial collective one. Then using the weights of experts, all individual preference relations are aggregated into a collective one. The consensus and proximity measures are calculated by using the relative projections of experts’ preference relations respectively. The consensus measure is used to guide the consensus process until the collective solution is achieved. The proximity measure is used to guide the discussion phase of consensus reaching process. In such a way, an iterative algorithm is designed to guide the experts in the consensus reaching process. Finally the expected value preference relations are defined to transform the interval collective preference relation to a crisp one and the weights of alternatives are obtained from the expected value preference relations. Two numerical examples are given to illustrate the models and approaches.     

How do we know who to include in collaborative research? Toward a method for the identification of experts

Collaborative research, defined as research involving actors participating in the problem situation under study, has an important role in operational research, strategic management and systems thinking. In a recent study, we found that a strong organizational focus incorporated into many soft operational research (OR) approaches is inadequate for studying societal problem situations, which are fragmented and have no clear boundary. Specifically, we failed to find a process of identifying individuals that is capable of representing the perspectives of actors and sufficient for research into societal problem situations. We found no clear terminology accounting for ontological differences between actors, individuals representing them and conceptual representations of acting entities. In response to this gap in the literature, we propose terminology that differentiates among actors (individuals or collective entities in the real world), experts (individuals capable of representing the perspective of an actor) and agents (ideal-typical representations of actors). Based on this terminology, we propose an iterative method to guide the assembly of an expert group to undertake collaborative research into societal problem situations. To demonstrate the application of our method, we present selected insights from our study in an electronic supplement.     

基于云模型和VIKOR扩展方法的电网企业科技发展区间值多属性群决策

国家电网公司确立了“一强三优”的战略目标,以公司总体发展战略为基石,集中力量开展科技发展和评估技术的相关研究。因此,对公司科技发展战略进行分析和评估是极其具有现实意义和理论意义的。本文提出了一种基于云模型和VIKOR扩展方法的群决策方法。将语言决策值综合为正态云模型,进而转化为区间值。通过VIKOR扩展方法构建可能度矩阵进行综合群决策。研究表明,针对准则值为语言值或区间值的多属性群决策,该方法可以更加客观有效。     

情绪影响下选时博弈模型及其最优策略

依据Quiggin的秩依期望效用理论研究经典选时博弈问题。通过引入可以刻画局中人在博弈中情绪状态的非线性决策权重函数,将RDEU有限策略博弈扩展到连续博弈,构建了RDEU选时博弈模型。基于Riccati微分方程的解法,求出博弈模型中局中人的最优策略。最后,通过数值仿真,分析了不同情绪状态对局中人博弈决策行为的影响。研究发现,情绪对混合策略意义下的局中人最优策略有着显著的影响,在乐观情绪状态下,局中人对混合策略极易产生自信和较高的信任倾向,表现出"风险爱好者"行为;在悲观情绪状态下,局中人往往对混合策略缺乏自信和信任,表现出“风险厌恶者”行为。     

General communication schemes for multiobjective decision making

In this paper four general communication schemes for multiobjective decision making are presented. The procedures are completely general and make no presumptions about linearity or convexity. Nevertheless, improving bounds on the optimal value are available in each iterative step. Hereby, the procedures model the essential judgements to make and the necessary information to exchange between the decision maker and an analyst in order to make the communication progress. The procedures provide valuable theoretical insight. They also supply a framework for synthesizing known and developing new procedures.     

QUANTITATIVE METHODS FOR DECISION SUPPORT IN THE ICELANDIC FISHING INDUSTRY

Abstract Modern fisheries already collect vast amount of data, for example, through ERP systems and electronic log‐books. It is however well known that the data are rarely used for improving operational decision making. By converting this data into useable information, decision making in the fishing industry could be improved. This paper attempts to show that quantitative methods can be of use in many aspects of decision making in the fishing industry. The paper proposes a hierarchically structured decision support process based on two different optimization models for supporting the long‐ and short‐term decision making in fisheries. For long‐term planning the paper proposes a linear optimization model that describes the entire operation of a vertically integrated seafood company. For short‐term decision making the paper proposes a mixed integer linear optimization model to assist in organizing vessel trips and deciding catch location with regard to raw material quality and yield obtained in processing.     

The construction of a comprehensive model for production strategy evaluation

To acquire reasonable profits and to be competitive in the globalize market, more and more firms today have exploited various kinds of production strategies, such as outsourcing, joint venture, or some kind of strategic alliance. Since every production strategy has its benefits and costs and may bring a firm different opportunities and risks, which kind of production strategy is the most suitable for a firm to carry out is a difficult and complicated decision with a high degree of uncertainty. Therefore, this research proposes a comprehensive production strategy evaluation model that can facilitate such a decision making for a firm. The factors that should be considered for devising a production strategy are listed for the benefits, opportunities, costs and risks (BOCR) merits first, and fuzzy Delphi method is applied next to select the most important factors under each merit. A network with BOCR merits is constructed to incorporate the selected factors, and fuzzy analytic network process is then applied to consider the interdependence and feedback inside the network. The proposed model can effectively aid decision making on which kind of production strategy should be adopted by a firm. A case of a printed circuit board manufacturer is presented for the illustration of the proposed model.