基于合作博弈模型的公共河流水资源分配方案研究

水资源的稀缺性使得公共河流的水资源分配问题中存在着各种冲突与矛盾。为了确定公共河流流经的各用水主体之间分配水资源的合理方案，本文建立具有外部性的合作博弈模型来分析公共河流流经的各用水主体之间在竞争与合作并存情况下的水资源分配问题。使用动态博弈的方法来确定联盟之间通过竞争产生的均衡水资源分配量，然后在各联盟之内通过Nash协商的方式来分配联盟的均衡水资源分配量，比较各种方案下用水主体产生的总效用，进而得到公共河流用水主体之间竞争与合作并存时的最优水资源分配方案和各用水主体之间形成联盟的具体形式。研究表明：受到外部性环境的影响，公共河流用水主体之间部分合作可能比完全合作产生更大的总效用，合作与竞争并存时的最优分配方案优于完全合作时的最优分配方案。     

Why Do Countries Regulate Environmental Health Risks Differently? A Theoretical Perspective

Why do countries regulate, or prefer to regulate, environmental health risks such as radiofrequency electromagnetic fields and endocrine disruptors differently? A wide variety of theories, models, and frameworks can be used to help answer this question, though the resulting answer will strongly depend on the theoretical perspective that is applied. In this theoretical review, we will explore eight conceptual frameworks, from different areas of science, which will offer eight different potential explanations as to why international differences occur in environmental health risk management. We are particularly interested in frameworks that could shed light on the role of scientific expertise within risk management processes. The frameworks included in this review are the Risk Assessment Paradigm, research into the roles of experts as policy advisors, the Psychometric Paradigm, the Cultural Theory of Risk, participatory approaches to risk assessment and risk management, the Advocacy Coalition Framework, the Social Amplification of Risk Framework, and Hofstede's Model of National Cultures. We drew from our knowledge and experiences regarding a diverse set of academic disciplines to pragmatically assemble a multidisciplinary set of frameworks. From the ideas and concepts offered by the eight frameworks, we derive pertinent questions to be used in further empirical work and we present an overarching framework to depict the various links that could be drawn between the frameworks.     

Defining Dangerous Anthropogenic Interference: The Role of Science, the Limits of Science

Defining "dangerous anthropogenic interference with the climate system" in the context of Article 2 of the UN Framework Convention on Climate Change (UNFCCC) presents a complex challenge for those developing long-term climate policy. Natural science has a key role to play in quantifying vulnerabilities of elements of the Earth system and estimating the risks from a changing climate. But attempts to interpret Article 2 will inevitably draw on understanding from social science, psychology, law, and ethics. Here I consider the limits of science in defining climate "danger" by focusing on the potential disintegration of the major ice sheets as an example of an extreme impact. I show that considerations of timescale, uncertainty, and learning preclude a definition of danger drawn purely from natural science. Decision makers will be particularly challenged by one characteristic of global problems: answers to some scientific questions become less accurate over decadal timescales, meandering toward the wrong answer, a feature I call negative learning. I argue for a precautionary approach to Article 2 that would be based initially on current, limited scientific understanding of the future of the ice sheets.     

Taking (and Sharing Power): How Boards of Directors Can Bring About Greater Fairness for Dependent Stakeholders

One of the ways in which scholars have sought to broaden the discussion of the social responsibilities of corporations and their managers is through the development of the stakeholder concept. The primacy of shareholder interests in corporate‐governance processes and managerial action is, however, a myth that justifies all sorts of managerial self‐interest seeking and exploitation of particular stakeholder groups. What makes this myth particularly problematic—from the standpoint of fairness and corporate governance—is that not all nonshareholder stakeholders are equally situated with regard to their ability to secure fair treatment. In this article, I explore the ethical dimensions of board responsibilities to dependent stakeholder groups by first describing the differences between shareholders and nonshareholder stakeholders with regard to risk, examining why dependent stakeholders (stakeholders with legitimate and urgent claims, but no power) are particularly important from the standpoint of stakeholder risk, and discussing how stakeholder consultation might provide a partial fix to such problems. I will conclude with proposals for how boards can more faithfully discharge their ethical responsibilities to dependent stakeholder groups, and in so doing facilitate stakeholder involvement in corporate governance in ways that promote fairness in organization–stakeholder relationships.     

Belief in Public Efficacy, Trust, and Attitudes Toward Modern Genetic Science

Government and policymakers want to engage the public in a dialogue about the conduct and consequences of science and increasingly seek to actively involve citizens in decision-making processes. Implicit in this thinking is that greater transparency and public inclusion will help dispel fears associated with new scientific advancements, foster greater public trust in those accountable, and ultimately increase the acceptability of new technologies. Less understood, however, are public perceptions about such high-level involvement in science and how these map onto public trust and attitudes within a diverse population. This article uses the concept of public efficacy -- the extent to which people believe that the public might be able to affect the course of decision making -- to explore differences in trust, attentiveness, and attitudes toward modern genetic science. Using nationally representative data from the 2003 British Social Attitudes Survey, we begin by examining the characteristics of those who have a positive belief about public involvement in this area of scientific inquiry. We then focus on how this belief maps on to indicators of public trust in key stakeholder groups, including the government and genetic scientists. Finally, we consider the relationship between public efficacy and trust and attitudes toward different applications of genetic technology. Our findings run contrary to assumptions that public involvement in science will foster greater trust and lead to a climate of greater acceptance for genetic technology. A belief in public efficacy does not uniformly equate with more trusting attitudes toward stakeholders but is associated with less trust in government rules. Whereas trust is positively correlated with more permissive attitudes about technologies such as cloning and gene therapy, people who believe in high-level public involvement are less likely to think that these technologies should be allowed than those who do not.     

Incorporating balance concerns in resource allocation decisions: A bi-criteria modelling approach

We consider resource allocation problems where inputs are allocated to different entities such as activities, projects or departments. In such problems a common goal is achieving a desired balance in the allocation over different categories of the entities. We propose a bi-criteria framework for trading balance off against efficiency. We define and categorise indicators based on balance distribution and propose formulations and solution algorithms which provide insight into the balance-efficiency tradeoff. We illustrate our models by applying them to the data of a portfolio selection problem faced by a science funding agency and to randomly generated large-sized problem instances to demonstrate computational feasibility.     

Stakeholder value disclosures: anchoring on primacy and importance of financial and nonfinancial performance measures

In the growing debate about stakeholder values, there has been little discussion about information overload or whether the requested disclosures can be effectively used. Stakeholder advocates call for complicated and massive environmental and related social disclosures while not considering how information overload might affect the discourse about corporate performance. Stakeholders, including shareholders, plead for more transparency in financial statements, management discussion and analysis (MDA), and other corporate disclosures. As we know, shareholders and boards of directors are most concerned with the ‘Holy Trinity’ of earnings per share, dividends and market value changes. We believe that managers and stakeholders involved in performance evaluations have multiple interests that extend beyond traditional shareholder value measures. We note that the Balanced Scorecard (BSC) was developed as one tool to reflect and communicate these multiple measures. We test how managers use (or ignore) multiple performance measures and we posit that stakeholders will face many of the same constraints when using and processing multiple disclosures including Corporate Social Reports (CSR), environmental, or similar disclosures. While we do not directly test a wide variety of stakeholder disclosures, we examine eight (four for a single subject) shareholder values (financial measures) and four stakeholder values (nonfinancial measures). The eight measures included in our research instruments serve as proxies for the multiple concerns that might be of interest to many stakeholders. Note that stakeholders are likely to be extremely interested in nonfinancial performance measures, while many shareholders will likely concentrate on financial performance measures. Field research has reported managers tend to favor financial measures while discounting or ignoring nonfinancial measures when evaluating subordinates, making it difficult to align performance evaluations and incentives with corporate strategies (Ittner et al. Account Rev 78:725–758, 2003). In this study, we find the relative weights managers place on financial and nonfinancial performance measures are influenced by both (1) presentation order and (2) the relative importance of specific measures. When financial measures are presented first, the manager who performs better on financial measures is rated higher than the manager who performs better on nonfinancial measures. However, when nonfinancial measures are presented first, managers who excel on nonfinancial measures are rated higher. Reports that include financial measures that are relatively more (less) important also produce higher (lower) ratings for the manager who excels on financial measures. Thus, the relative weights that superiors place on financial and nonfinancial measures in evaluating corporate managers’ performance are substantially anchored both by the order in which measures are presented as well as by the importance of the specific performance measures employed. Other stakeholder disclosures are likely to be similarly anchored, perhaps biased, by primacy and a priori importance rankings.     

Making ‘my’ problem ‘our’ problem: Warfare as collective action,and the role of leader manipulation

Warfare is a collective action problem, and groups often stand to benefit from the quick and coordinated action that leaders can provide. This basic principle is as true in modern political contexts as it has been across our evolutionary history, and there is growing evidence that leadership has evolved, in part, to solve such collective action problems. Despite the material and reproductive benefits of leadership for groups, leaders may also seek private gains at the expense of group interests. Drawing upon insights from social and evolutionary psychology, I explain how leaders solve collective action problems in warfare, but also how leaders manipulate audience preferences when their own interests do not align with group interests. Specifically, when leaders anticipate great private gain from foreign aggression while facing steep public resistance at home, leaders will misframe the conflict as defensive rather than offensive in nature. I provide an evolutionary analysis that explains why leaders exploit this framing specifically, and I identify the specific aspects of conflict framing that are most likely to be exploited toward this end.     

Characterizing the Structure of Optimal Stopping Policies

This paper studies a stochastic model of optimal stopping processes, which arise frequently in operational problems (e.g., when a manager needs to determine an optimal epoch to stop a process). For such problems, we propose an effective method of characterizing the structure of the optimal stopping policy for the class of discrete‐time optimal stopping problems. Using this method, we also derive a set of metatheorems that can help identify when a threshold or control‐band type stopping policy is optimal. We show that our proposed method can determine the structure of the optimal policy for some stopping problems that conventional methods fail to do so. In some cases, our method also simplifies the analysis of some existing results. Moreover, the metatheorems we propose help identify sufficient conditions that yield simple optimal policies when such policies are not generally optimal. We demonstrate these benefits by applying our method to several optimal stopping problems frequently encountered in, for example, the operations, marketing, finance, and economics literatures. We note that with structural results, optimal‐stopping policies are easier to follow, describe, and compute and hence implement. They also help determine how a stopping policy should be adjusted in response to changes in the operational environment. In addition, as structural results are critical for the development of efficient algorithms to solve optimal stopping problems numerically, we hope that the method and results provided in the study will contribute to that effort.     

Milton keynes: A model for regenerating our cities?

Milton Keynes is one of the most succesful of Britain's new towns. With new towns such as Peterborough and Telford, this can be seen as the latest stage of the New Town Movement which started at the end of World War II. Of these new towns, Milton Keynes is the largest and the most ambitious in its objectives. The problems facing the United Kingdom today are very much greater than when Milton Keynes was first started but, because of a flexible planning approach, the Development Corporation has responded to these changes and has established policies to assist in solving these problems. The key to this success is a more flexible and effective manufacturing base. Today we face unprecedented changes, unique in our history. Although manufacturing and services will provide the wealth to solve these problems, we will only succeed through co-operation between the private sector and local and national government. A study of Milton Keynes, its growth, objectives, successes and failures could provide a useful model for our future planning.     

A general space-time model for combinatorial optimization problems (and not only)

We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable x_ilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example.     

On the scientific study of small samples: Challenges confronting quantitative and qualitative methodologies

Often phenomena that are important to understand and predict are very rare. Rare events can prove difficult to analyze systematically because they do not generate many sampling observations. In this article I examine how small sample sizes can be studied scientifically. The article begins with an explanation of the distinction between research and science. I then bring to the fore the importance of counterfactual comparisons and outline the nature of the methodological problems posed by the study of small samples. These problems include challenges related to using a single case, small sample sizes, selecting on the dependent variable, regression toward the mean, explaining a variable with a constant, and using the same data to both generate and test hypotheses. I provide potential resolutions to these problems which are: (a) employing matched controls; (b) shifting or widen the category of inquiry; (c) selecting variables based on variance in the independent variable; (d) including counterfactuals; (e) ensuring that both independent and dependent variables demonstrate variation; and (f) testing potential hypotheses against data sets that are fully independent of those used to generate the hypotheses. I conclude with a discussion of future directions for undertaking a more scientific approach to using small samples.     

Scientific ethics and publishing conduct

The publishing conduct of scientists is increasingly becoming the object of science-based ethical considerations. Studies have shown that questionable publishing conduct is not a rare phenomenon in this area. National and international scientific organisations are making recommendations about appropriate scientific publishing conduct and, in doing so, are referring to universal ethical principles. A number of science-related ethical norms, for example the prohibition to fabricate or manipulate data, can be justified for all sciences by the goal of knowledge and common epistemological assumptions. On the other hand, other science-related ethical norms, such as rules governing the assignment of authorship and the prohibition of plagiarism, can be justified by means of a science-specific incentive mechanism, which was only explicitly analysed for the first time in detail by Merton (The sociology of science. University of Chicago Press, Chicago, pp. 286–324, 1957/1973): the attainment of recognition in the scientific community through published original articles on scientific progress. Particularly in conjunction with forms of remuneration based on this, this incentive mechanism leads to the temptation to make one’s own contribution to the development of science greater than is actually justified.     

Why RDAs and ULs Are Incompatible Standards in the U‐Shape Micronutrient Model: A Philosophically Orientated Analysis of Micronutrients' Standardizations

Risk assessments of micronutrients are carried out in the customary deficiency‐excess model. It is regarded as straightforward and unambiguous. Nevertheless, it is a problematic amalgamation of two different and to a certain extent contrasting perspectives on risk and science that we will criticize in this contribution. Our critique is framed in a conceptual scheme of opposing perspectives highlighted by the rival characteristics of RDAs and SULs and the role of science therein. The one part of our scheme holds the typically modern approach that centers on risks that can be scientifically assessed more or less confidently. Subsequent policies are aimed at preventing major health problems that affect the majority of the population from early on in life. The RDAs are the ideal type‐case here. The other part of our scheme holds a much more postmodern approach in which health risks are explicitly recognized as “uncertain.” Dealing with those risks has little to do with major health problems from early on in life. Here, we encounter the scientific quandary of disentangling complex factors and impacts that may relate to some extra quality of life later on in life. SULs are exemplarily thereof. We will show that RDAs originally spawned from the scientific aim of securing objective knowledge “to lay down the requirements of an adequate” diet. SULs, conversely, are the upshot of generating acceptable outcomes driven by ever‐increasing safety requirements. This shift from securing objective knowledge to generating acceptable outcomes will be addressed in relation to precautionary culture.     

Present positions and future prospects in management science

This paper offers some thoughts on the present state of management science as a discipline and a profession, and considers possible developmental strategies. The growth of management science, and the optimism this engendered, was largely premised on the successful use of scientific methodology and quantitative techniques to solve a relatively narrow range of management problems. In the 1970s, however, this positivist/quantitative ‘traditional management science’ became subject to increasing critical assault from those who wanted to broaden the impact of the discipline. Alternative management science approaches were born—soft systems thinking, organisational cybernetics, critical management science—and succeeded in establishing themselves. The existence of these alternative strands of work, alongside traditional management science, raises important questions about the future development of the discipline and profession. In this paper four developmental possibilities are set down and examined to see what future prospects they hold out for management science. It is argued that three of these—the isolationist, imperialist and pragmatist strategies—would lead the discipline into a dead end. A pluralist option, however, offers excellent opportunities for successful, future development.     

On doing better science: From thrill of discovery to policy implications

In this position paper, I argue that the main purpose of research is to discover and report on phenomena in a truthful manner. Once uncovered, these phenomena can have important implications for society. The utility of research depends on whether it makes a contribution because it is original or can add to cumulative research efforts, is rigorously and reliably done, and is able to inform basic or applied research and later policy. However, five serious “diseases” stifle the production of useful research. These diseases include: significosis, an inordinate focus on statistically significant results; neophilia, an excessive appreciation for novelty; theorrhea, a mania for new theory; arigorium, a deficiency of rigor in theoretical and empirical work; and finally, disjunctivitis, a proclivity to produce large quantities of redundant, trivial, and incoherent works. I surmise that these diseases have caused immense harm to science and have cast doubt on the role of science in society. I discuss what publication gatekeepers should do to eradicate these diseases, to stimulate the undertaking of more useful and impactful research, and to provide the needed incentives to better align the interests of researchers with those of the greater good. Finally, I highlight where technical improvements are needed to enhance research quality, and call on deeper reflection, transparency, and honesty in how we do research.     

Linear solution schemes for Mean-SemiVariance Project portfolio selection problems: An application in the oil and gas industry

We study the Mean-SemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal risk-reward portfolio of non-divisible projects when the risk is measured by the semivariance of the portfolio׳s Net-Present Value (NPV) and the reward is measured by the portfolio׳s expected NPV. Similar to the well-known Mean-Variance portfolio selection problem, when integer variables are present (e.g., due to transaction costs, cardinality constraints, or asset illiquidity), the MSVP problem can be solved using Mixed-Integer Quadratic Programming (MIQP) techniques. However, conventional MIQP solvers may be unable to solve large-scale MSVP problem instances in a reasonable amount of time. In this paper, we propose two linear solution schemes to solve the MSVP problem; that is, the proposed schemes avoid the use of MIQP solvers and only require the use of Mixed-Integer Linear Programming (MILP) techniques. In particular, we show that the solution of a class of real-world MSVP problems, in which project returns are positively correlated, can be accurately approximated by solving a single MILP problem. In general, we show that the MSVP problem can be effectively solved by a sequence of MILP problems, which allow us to solve large-scale MSVP problem instances faster than using MIQP solvers. We illustrate our solution schemes by solving a real MSVP problem arising in a Latin American oil and gas company. Also, we solve instances of the MSVP problem that are constructed using data from the PSPLIB library of project scheduling problems.     

The status of the systems approach

This paper is an assessment of the attempts by Ackoff, Beer and Churchman to provide a justification for the claim that OR is a science. These writers tackle the problem using the philosophies of positivism, pragmatism and conventionalism/idealism in an attempt to build an ideal scientific methodology for OR. The conclusions reached are that no such methodology can exist and that philosophy confuses rather than clarifies the nature of OR as a science. The weakness of the philosophical approach to some of the problems faced by OR is demonstrated through a discussion of Ackoff and Emery's analysis ‘On Purposeful Systems’. An outline is given of an alternative framework through which the problems of practising OR can more fruitfully be viewed.     

Small Theories and Large Risks—Is Risk Analysis Relevant for Epistemology?

Ought we to take seriously large risks predicted by “exotic” or improbable theories? We routinely assess risks on the basis or either common sense, or some developed theoretical framework based on the best available scientific explanations. Recently, there has been a substantial increase of interest in the low‐probability “failure modes” of well‐established theories, which can involve global catastrophic risks. However, here I wish to discuss a partially antithetical situation: alternative, low‐probability (“small”) scientific theories predicting catastrophic outcomes with large probability. I argue that there is an important methodological issue (determining what counts as the best available explanation in cases where the theories involved describe possibilities of extremely destructive global catastrophes), which has been neglected thus far. There is no simple answer to the correct method for dealing with high‐probability high‐stakes risks following from low‐probability theories that still cannot be rejected outright, and much further work is required in this area. I further argue that cases like these are more numerous than usually assumed, for reasons including cognitive biases, sociological issues in science and the media image of science. If that is indeed so, it might lead to a greater weight of these cases in areas such as moral deliberation and policy making.     

An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems

The main characteristic of today's manufacturing environments is volatility. Under a volatile environment, demand is not stable. It changes from one production period to another. To operate efficiently under such environments, the facilities must be adaptive to changing production requirements. From a layout point of view, this situation requires the solution of the dynamic layout problem (DLP). DLP is a computationally complex combinatorial optimization problem for which optimal solutions can only be found for small size problems. It is known that classical optimization procedures are not adequate for this problem. Therefore, several heuristics including taboo search, simulated annealing and genetic algorithm are applied to this problem to find a good solution. This work makes use of the ant colony optimization (ACO) algorithm to solve the DLP by considering the budget constraints. The paper makes the first attempt to show how the ACO can be applied to DLP with the budget constraints. In the paper, example applications are presented and computational experiments are performed to present suitability of the ACO to solve the DLP problems. Promising results are obtained from the solution of several test problems.