Socially Responsible Modeling: A Stakeholder Approach to the Implementation of Ethical Modeling in Operations Research
Matthew J. Drake (corresponding author) Virginia W. Gerde David M. Wasieleski
Palumbo and Donahue Schools of Business Duquesne University 600 Forbes Avenue 925 Rockwell Hall Pittsburgh, PA 15282 Phone: 412-396-1959 Fax: 412-396-1797 Email:
[email protected]
Submitted for consideration for publication in OR Spectrum: July 2008
Abstract A common dilemma for modelers in Operations Research involves how to construct ethically sensitive models. Concern for ethical modeling has recently become more widespread in the OR literature. Arguably, however, this concern has not manifested into concrete frameworks for analyzing models. This paper presents an approach from the organizational ethics field for evaluating models. After first reviewing the state of ethics in OR, a stakeholder framework for evaluating the social performance of the model is presented. The normative core underlying stakeholder theory addresses the ethical concerns in decision support systems and provides a prescriptive solution to ethical issues in modeling.
Keywords: ethical decision making, management science, operations research, social performance, stakeholders
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Socially Responsible Modeling: A Stakeholder Approach to Ethical Modeling in Operations Research 1 Introduction Ten years ago, the Journal of Business Ethics published an interview with the celebrated scholar, philosopher, ethicist, and epistemologist, C. West Churchman, to commemorate his accomplishments in the management community (van Gigch, Koenigsberg, and Dean, 1997). This dialogue took place on Dr. Churchman’s 80th birthday in honor of his contributions on quality control in operations and in management. In his reflections about his career, Churchman admitted that the greatest challenge facing management is how to ensure that a firm’s product or service always reaches the end user safely and ethically. He devoted his professional life to the pursuit of how to improve the human condition and overall quality of life. In this conversation, however, he lamented that science has never been able find a way to serve all human beings who have an ethical need for technological systems. He called on management and operations researchers to reorient the nature of Total Quality Control away from being product-focused and instead be directed towards serving the ethical nature of the market. It seems fitting to revisit Dr. Churchman’s vision a decade later to examine the state of ethics in Operations Research (OR). Discussions addressing ethical concerns in Operations Research have become more prevalent in recent years since this interview was conducted (Brans and Gallo, 2007; Le Menestrel and Van Wassenhove, 2004; Gallo, 2004). Researchers in the field identify the need for a discourse about ethics in terms of the development and implementation of OR models. This recent awareness is well-intentioned and commendable; however, it is only a preliminary step in actually addressing how ethics can be implemented in OR research and practice. This paper advocates the application of ethical principles to modeling in OR, but takes a pragmatic approach for its utilization. It is not outlandish to claim that this is rather uncharted territory in OR; that is, the application of a framework and process for evaluating the ethical efficacy of models in practice will be useful. The purpose of this paper is to provide a framework for developing and using models in an ethical, or responsible, way. In OR, modeling serves the purpose of helping individuals make decisions. Here we find common ground between OR modeling and business ethics. One of the main purposes of business ethics research is “to serve as a guide to ethically sound decision making…” 3
(Robertson, 1993, p.594). Within the business ethics paradigm, normative stakeholder theory is a widely used strategy for business ethicists to make morally responsible decisions across a variety of applications (Wijnberg, 2000). Stakeholder theory can be applied to multiple disciplines that deal with managerial decision making and holds importance to any scholar with a social science orientation (Jones and Wicks, 1999). In this paper we briefly describe the importance of ethics in the use and implementation of models as part of the decision support system (DSS). We then introduce stakeholder theory as a perspective or approach by which the social performance of a decision can be examined. Using stakeholder theory in the context of OR modeling, we present a multi-stakeholder approach specifically for OR modeling and decision making based on theory development from the business ethics literature. Our stakeholder approach takes the form of a social performance framework that incorporates the consideration of principles, processes, and outcomes as inputs to the decision-making process. Using this framework we map previous OR and ethics literature and propose areas for future research. 2 History of ethics in operations research Gass and Assad (2005) trace the foundation of the field of Operations Research (or “Operational Research” in the UK) back to the British Air Ministry’s establishment of the Bawdsey Manor Research Station in Suffolk. The charge of this organization was to investigate the use of radar to cut off approaching enemy aircraft. While others cite other dates such as 1937 and 1939 as the “birthday” of OR, there seems to be general consensus that the field was founded in the 1930s through research in military operations. Given the moral purpose of this research for protecting human life, it can be deduced that the field of operations research has been intertwined with ethics from the start through its application to the operations of warfare. Since its founding in the 1930s, the field of OR has matured among similar fields such as management science (MS), operations management (OM), and applied mathematics. This has caused some confusion within the general public, who are often the customers of OR analysis, as to the actual definition of the OR field of study. This confusion, in part, has led the Institute for Operations Research and Management Science (INFORMS) to launch a marketing and promotion campaign known as “The Science of Better” in late 2003. According to the website for the program (INFORMS, 2007), OR is “the discipline of applying advanced analytical 4
methods to help make better decisions.” This definition of OR echoes other definitions (see, e.g., Rardin (1998) and Muller-Merbach (2002)) by emphasizing the application of mathematical models to decision-making. It is clear, however, that some OR academics and practitioners have forgotten this important point—that OR is, by definition, a practical field that is supposed to help people make better decisions. Admittedly, it is easy for operations researchers to get so caught up in developing faster algorithms or progressively elegant solutions to increasingly irrelevant problems in today’s complex world that they forget that the entire impetus for the field is predicated on aiding decision makers. Koch (2000) identifies this problem in OR by telling several cringe-worthy anecdotes about these types of studies and presentations that he has seen in the past. It is interesting to note that ethics was a prime concern of several OR pioneers in the 1950s, such as C.W. Churchman, especially considering that this interest generally waned in the subsequent decades (1970). As evidence of the de-emphasis of OR ethics in the latter half of the Twentieth Century, Gass and Assad’s Annotated Timeline of Operations Research (2005) only contains one reference to ethics in its index; that is the establishment of the PROMETHEUS group at the EURO XVIII Conference held in Rotterdam in 2001. The PROMETHEUS group grew out of a movement of mainly European OR academics and practitioners who had started to reconsider the ethical implications of OR practice, an issue that still seems to elude the majority of OR professionals in the United States and the rest of the world. With this in mind, it is not surprising that Gass and Assad (2005), who work in the U.S., did not mention any other developments in their history of OR. Brans and Gallo (2007) offer two main reasons why the interest in the ethics of OR has increased over the past decade. The first reason is that OR has now become a mature scientific discipline after over fifty years. Researchers in younger disciplines spend the bulk of their time developing new theories and methodologies; only mature disciplines afford a significant amount of self-reflection. The second reason is that OR professionals such as Churchman, who have always been concerned with the ethics of OR, had become somewhat disillusioned with the current focus of the field being predominantly mathematical and not on the holistic decisionmaking process. 5
The current body of literature on the ethics of OR can be divided into two major groups. It is important to recognize the distinction between these two categories because they address fundamentally different sets of critical ethical issues. The first group of studies and analysis focus on the ethical development of models. Major issues include the documentation of modeling assumptions, model objectivity and freedom from advocacy, and the quality of data used in model building and validation. See Brans and Gallo (2007), Le Menestrel and Van Wassenhove (2004), and Kleijnen (2001) for discussions of the major ethical issues in model building. Many of these issues have been incorporated into several attempts to develop codes of ethics for OR professionals and their academic cousins in electrical and electronic engineering (Brans and Gallo, 2007; Gallo, 2004; Gass and Asad, 2005). The second category of literature on the ethics of OR concerns the ethical use of models in the decision-making process. Depending on the decision environment under consideration, the user of the model could be separate from the model builder physically and removed in time as well. We mainly consider ethical model use in this study because the usage phase of the modeling process presents the greatest possibility of realizing negative outcomes for one or several groups of stakeholders in a given decision and for promoting the betterment of social performance of models. Gallo implicitly advocates the ethical implementation of modeling when he states, “Operations research, as the science which claims to provide tools for helping decision making processes, may, more than many other scientific disciplines, have an impact on people’s lives and hence on society at large” (Gallo, 2004, p.470). This makes it all the more troubling that the OR community as a whole, save for a group of European OR researchers who participated in the 2002 EURO conference (Brans and Gallo, 2007) and subsequent Fontainebleau workshop (and, of course, a small group of independent researchers (Kleijnen, 2001; Le Menestrel and Van Wassenhove, 2004)), has largely ignored the ethical implications of OR practice. This reluctance of OR professionals to focus on the ethical implementation of models in practice unfortunately extends to implementation in general. In a 1994 survey, twelve OR experts emphasized usability of a model as the second most important quality of an effective model; but they failed to even mention implementation as one of the five most important qualities of an effective modeling process (Willemain, 1994). In a subsequent study observing 6
modeling protocols on formulation exercises, the experts spent only 2% of their time on implementation issues (Willemain, 1995). The two most important qualities of an effective modeler in the initial survey were “soft” skills such as the modeler’s mind-set and nontechnical expertise such as communication and teamwork skills. Even though these skills are recognized as extremely important, most collegiate OR/MS programs do little to cultivate them in their graduates (Willemain, 1994). These findings together suggest a general lack of big-picture introspection in the OR field as a whole. Many OR professionals acknowledge that there is more to being an effective operations researcher than building faster and faster models (for example), but few studies focus on understanding the implications of these issues for OR practice. Several studies that have addressed the ethical use of OR modeling in the decisionmaking process (e.g., Brans and Gallo, 2007; Le Menestrel and Van Wassenhove, 2004; and Gallo, 2004) have suggested that decision makers adopt a stakeholder perspective, thereby considering the potential implications of various strategies on interested constituencies. Theys and Kunsch state that “ethical management of the future implies the co-operation of all stakeholders towards a common long-term sustainability goal” (2004, p. 485). This is definitely a valuable and commendable approach to developing an ethical solution to a decision problem. Unfortunately, however, these articles basically conclude with the suggestion of adopting the stakeholder perspective without much guidance for actually doing it in practice. We add to this previous body of work by providing the foundation of stakeholder theory and a methodology for incorporating stakeholder considerations into the decision-making process along with OR modeling tools. These models do not make decisions; no OR model should ever do that in practice. In our discussion, we emphasize that OR models are merely an informational (but hopefully valuable) input to the decision-making process. As a result, we challenge Brans and Gallo’s conclusion that “the basic OR/MS models, and especially those belonging to the field of optimization, are not appropriate to providing assistance for the management of [systems facing the problems of mankind]” (2007, p. 167). That ethics is implicit in the development and use of decision support systems is clarified in Kleijnen (2001). However, there is no clear framework with which to map these articles or build on them to further this area. We add to this previous body of work by providing the foundation of stakeholder theory and a methodology for
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incorporating stakeholder considerations into the decision-making process along with OR modeling tools. 3 A discourse on ethics in operations research Ultimately, business ethics involves the improvement of “the moral conduct of business” (Kaler, 2000, p.264). This is in no way counterintuitive to the goals sought by operations researchers.
For OR researchers and professionals, ethics constitutes an awareness of the
impacts models have on people in society (Brans and Gallo, 2007) both in terms of desired effects as well as a concern for the side effects of the implementation of the model. It involves the realization that value judgments cannot be parsed out of science in general, and OR modeling, in particular (Gallo, 2004).
Business ethics and OR share the ultimate goal of
“securing the improvement of social systems” through management theory and research (Churchman, 1970; Singer and Singer, 1997). While “ethics” does not appear to be explicitly defined in the OR literature, the idea of “ethics” is based on two foundation principles—responsibility and sharing/cooperation (Gallo, 2004). These two principles advocated in the OR literature provide a good starting point for going beyond a mere discourse on ethics in modeling towards an actual framework for evaluating the social performance of models in use. Attributed to Jonas (1979) by OR researchers, the responsibility principle entails broadening the sense of duty beyond to only the client who commissioned operations research to include duty to a variety of individuals who could be affected by the implementation of the model. Thus, it implies that operations researchers are “responsible” to the people and groups who feel the impact of the work they conduct. The second principle, the sharing and cooperation principle, basically calls for more transparency in operations research. Essentially, this principle promotes the open distribution of the results of OR work (Gallo, 2004). Reminiscent of the accountability and transparency principle underlying the Sarbanes-Oxley Act of 2002, this principle in OR assumes a greater benefit can be served by allowing open access to information from scientific research. These two principles advocated in the OR literature provide a good starting point for going beyond a mere discourse on ethics in modeling towards an actual framework for evaluating the social performance of models in use. Their rationale mandates the consideration of 8
individuals beyond the users of the model. The responsibility principle calls for operations researchers to reflect on the societal effects from decisions made with the assistance of models. Moreover, the cooperation principle promotes the sharing of ideas and programs with anyone who has a “stake” in the research activity. Thus, together these principles justify the application of a stakeholder perspective when making decisions regarding the ethical efficacy of model implementation. We posit that stakeholder theory has an implicit normative core that makes it useful for making ethical decisions. The business ethics field has rejected the idea that ethics exists in an isolated dimension from business. The separation thesis states that the discourse of business and that of ethics can be distinguished apart. Keeping business and business ethics separate allows scholars to try to fill the void (Freeman, 1994). However, the danger of this forced dichotomy is that it gives business researchers the license to devise morally neutral theories, “which can be used to justify a great deal of harm” (Freeman, 1994, p.412). Logically speaking, a multifiduciary perspective of the firm suggests that the discourse of business and ethics are intertwined. Freeman’s (1984) stakeholder theory proposes that managers consider stakeholders in the development and implementation of business strategy, calling on managers to consider a multiplicity of external and internal constituents. Originally, a stakeholder was defined as “any group or individual who can affect or is affected by the achievement of the firm’s objectives” (Freeman, 1984, p.25). Traditionally, the primary stakeholders for an organization are the customers, the suppliers, the investors, and the employees. Stakeholder groups can be the media, the community, the natural environment, governments, competitors, creditors, and special interest groups. For a given firm or issue, the more influential stakeholder groups may vary. For instance, in developing a short supply chain, community and the natural environment may be influential stakeholder groups as well as suppliers. Stakeholder theory rejects the separation thesis of “business” and “ethics.” The underlying assumption is that the context of business theory is indeed, moral in nature. “For the purposes of business ethics, stakeholding has to be ultimately about improving the moral conduct of business” (Kaler, 2002, p. 93). The idea behind the separation thesis in the business and society realm is that it is valuable to isolate conversations about business and those about ethics. 9
Freeman rejects the separation thesis and instead strongly suggests that “it is not meaningful to talk about either stockholders or stakeholders without engaging in discourse that is at once normative, descriptive, instrumental and metaphorical” (Freeman, 1994, p.413). The same can be said of operations research. Only recently did a discourse about ethics even enter into the same conversation with modeling among mainstream researchers and practitioners. The primary argument for inclusion of stakeholders in business decision-making was instrumental -- because the stakeholder may (now or in the future) affect the business and thus the stockholders’ value. Later work used stakeholder theory in a descriptive way, more of a stakeholder approach or tool for analyzing the external business environment in terms of groups that may impact or be impacted by the firm. In the 1990s, several scholars offered the normative aspect of stakeholder theory – stakeholders ought to be considered in decision-making for their intrinsic value regardless of whether they have legitimate interests in the firm. Donaldson and Preston (1995) provide a useful conception of stakeholder theory by describing the descriptive, instrumental, and normative dimensions of the latter explained as follows: ... its fundamental basis is normative and involves acceptance of the following ideas: (a) Stakeholders are persons or groups with legitimate interests in procedural and/or substantive aspects of corporate activity. Stakeholders are identified by their interests in the corporation, whether or not the corporation has any corresponding functional interest in them. (b) The interests of all stakeholders are of intrinsic value. That is, each group of stakeholders merits consideration for its own sake and not merely because of its ability to further the interests of some other group, such as the shareowners (p. 67). The stakeholder perspective or approach allows for consideration of persons and groups besides the owners of a corporation. Described as a web of relationships, the stakeholders of a firm have interactions with the firm but also each other. For instance, the people in a geographic community may influence the local government, which may then enact legislation or a tax to influence the firm’s behavior. Counter to the position taken by Donaldson and Preston (1995) that stakeholder theory needs to be viewed in three parts—descriptive, normative, and instrumental, we advocate a convergent stakeholder theory, which states the three are interdependent and are best conceptualized collectively. 10
Jones (1995) proposed an “instrumental stakeholder theory” which he hoped would serve to synthesize the business and society field and connect ethics with economics and strategy. Though Jones’ insights mainly pertain to the competitive advantage stakeholder theory can provide to firms, it is relevant here for the connection its draws from stakeholder theory to ethics. Instrumental stakeholder theory emphasizes the social contracts that firms implicitly form with stakeholders and how this can lead to ethical behavior. Specifically it “posits that trusting and cooperative relationships help solve problems related to opportunism” (p.432). In a later piece, Freeman (1999) builds on this value-laden notion by explaining the link between instrumental theory and ethics in terms of the processes and outcomes that are inexorably connected to one another. He argues that the normative aspects of science cannot be separated from the instrumental aspects. “The very idea of a purely descriptive, value-free, or value-neutral stakeholder theory is a contradiction in terms (1999, p. 234). It is this integration of principles, procedures, and consequences that forms the basis of our evaluative framework for modeling. In stakeholder theory, management should (or does) consider the interests of stakeholder groups, but this does not mean the all stakeholder demands are met. Rather, the ethical decision maker takes into account considerations of the stakeholder, chooses the better ethical option and works to mitigate the potential negative consequences. For OR modeling, several scholars have already addressed stakeholder concerns (Cordoba and Midgley, 2006; Kirkwood, et al., 2005) and called for inclusion of stakeholder interests in decision making and therefore the development and use of models (Le Menestrel and Van Wassehoven, 2004; Theys and Junsch, 2004). Kleijnen succinctly states: “values are related to the purposes of the model” (Kleijnen, 2001, p. 225). In this paper we use stakeholder theory as a foundation for a framework with which to guide decision making for using and implementing an operations research model. Originally stakeholder theory was viewed as an aggregation of hub-and-spoke relationships, where the firm is in the middle (the hub) of the diagram, and there are two-way arrows going from the firm to various stakeholder groups. These two-way arrows depicted the impact of the firm on the stakeholder group as well as the possible impact of the stakeholder group on the firm. In this sense, the firm had several independent, dyadic relationships with stakeholder groups concurrently. Consideration of stakeholder interests in decision making with this diagram can be viewed as the sum of the stakeholder impacts. 11
However, this concept does not provide for the possible interconnections of the firm’s stakeholder groups with each other. For instance, the community may influence the media to make a particular action or policy more widely known, adding to a controversy. The conceptual diagram did not provide for some people having a role in more than one stakeholder group, such as employees living in the community, or employees as shareholders. Therefore, a subsequent conceptualization of a stakeholder diagram incorporates these interconnections and looks more like a spider web. Each strand of the web represents a dyadic relationship that is influenced by both groups, dynamic in nature, and influenced by the changes in other relationships through the actions of the stakeholder group. Therefore, for decision making to take into account various stakeholder groups, the direct impact (direct dyadic relationship) and the indirect impacts (via other stakeholder relationships) should be considered. If a broader group of stakeholder interests is considered when making decisions, there is a greater likelihood that ethically responsible outcomes will result. 4 Socially responsible modeling framework Values are inherent in the framing, development, and use of models, so we propose a stakeholder approach to more accurately incorporate the “messy” “social complexity” (messy is adapted from Ackoff’s “messes” (Ackoff, 1979); social complexity is from Le Menestrel and Van Wassehove (2004)). When an OR model is developed and used for decision making, a consideration of stakeholder interests should be conducted, not to please all the stakeholder groups, but rather to reach a more ethical outcome. Although Theys and Kunsch (2004) call for “co-operation between stakeholders,” it is not always practical to have such real-time cooperation; however, engaged consideration of stakeholder concerns is one way the modeler or decision maker can approximate, but not substitute for, this co-operation. We adapt Wood’s (1991) corporate social performance model to the stakeholder approach to develop a framework for assessment of socially responsible modeling (SRM). Wood’s original corporate social performance model allows for performance to be viewed as neither inherently good nor bad, but rather the model allowed for “a construct for evaluating business outputs that must be used in conjunction with explicit values about appropriate business-society relationships” (1991, pp. 693-694). Wood (1991) identified three components of corporate social performance: principles, processes, and outcomes. These components translate 12
well for assessing the principles of responsible modeling, processes involved in responsible modeling, and outcomes of the development and use of models. Ethical decision making in modeling and the actual use of models then consists of consideration of stakeholders in terms of principles, processes, and outcomes for overall SRM (see Table 1). INSERT TABLE 1 ABOUT HERE The SRM framework itself does not make value judgments as to what values, stakeholders, outcomes, or ethical principles take priority. Rather, it is the modeler and decision maker that use the SRM framework as a tool to evaluate the ethical principles, processes and outcomes associated with the model and its use as a decision tool. Not all stakeholders’ needs and wants will be able to be addressed concurrently; however, an ethical application of a model entails recognition of the weaknesses of the model or decision and ways to mitigate such negative consequences. In a broad generalization, the SRM framework can be likened to Brans’ (2004) elements of ethical management: ‘Respect’ elaborated as the principles, ‘Multicriteria Management’ more broadly considered as processes, and “Happiness’ assessed as outcomes, necessarily including the economic, social, and environmental impacts. 4.1 Principles of responsible modeling
“In short, first do the right thing and second, do it right.” (Keeney, 2002, p.935)
The first part of our framework—the principles of responsible modeling—are illustrated in the first section of Table 1. We begin our discussion by returning to Gallo’s (2004) description of one of the main ethical principles already advocated in the OR literature: responsibility. Since the SRM framework is understood in terms of the stakeholders affected by models in use, the responsibility principle is an appropriate starting point for organizing the deontological principles that promote responsible processes and outcomes associated with a model. Therefore, under this category we identify several principles as a guide for ethical considerations: rights and duties, justice/fairness, and ethics of the good and ethics of the right. While these ethical principles are not all inclusive, we believe they serve as substantial guidelines for general consideration. We describe all of these principles in more detail and provide examples from current OR literature in the following sections. 13
4.1.1 Duties and rights In OR, the honoring of individuals’ duties and rights are discussed indirectly. For instance, the sharing and cooperation principle applies to the results of the research activity itself. Any “ideas, algorithms, or software” that are generated by OR research are subject to this principle (Gallo, 2004, p.469). Since the outcomes of research activity contribute to a universal pool of scientific knowledge and information, modelers have an obligation to share their insights and results with other researchers. Thus, the ways in which models are created, what problems they address, and how they are implemented should be made available to the stakeholders of the research activity. Anyone in the scientific and professional communities that could be affected by or utilize this knowledge must have open access to it. In effect, this principle advocates transparency in the development of models so that more ideas are generated and proliferate at a faster pace, leading to a greater benefit to society. Modelers have a positive duty to cooperate with each other by sharing information to work towards societal betterment. This notion of duties and corresponding rights translates also to issues related to models in use. From a societal perspective, it is not acceptable for an entity to abuse its power. Since society has “certain expectations for appropriate business behavior and outcomes,” it follows that those entities have an obligation to society not to cause harm through their operations (Wood, 1991, p. 695). Theys and Kunsch (2004) discuss the importance of cooperation in the ethical management of stakeholders in OR. However, for them cooperation takes on a different perspective; one that focuses on a dialogue with all stakeholders affected by the model. To achieve happiness as an outcome of OR, modelers must “respect” varying stakeholder needs. In effect, concern for the rights of the stakeholders of the model should be garnered. The authors discuss the importance of respect for the rights of future generations when evaluating the ethical nature of a model. In violation of this ethical principle, the US Minerals Management Service (MMS) worked with a model whose objective was to maximize royalties from natural gas production on federally owned lands (Stoddard, 2005). This model concentrates on only one decision alternative—extracting and selling of reserves. Thus, depletion of natural resources is not explicitly addressed in Stoddard’s presentation of the modeling framework, nor is there a concern for future generations who will be affected by exploited natural gas reserves. 4.1.2 Justice and fairness 14
The justice/fairness principle deals with a comparative assessment of treatment across individuals or situations and the handling of outcomes (Greenberg, 1993; Rawls, 1999). “Justice, as an organizing principle, can be described as the value that best captures organizations’ efforts to meet the needs of a wide variety of internal and external stakeholders” (Gerde, 2001, p. 473). For determining whether a particular model is being utilized ethically, the perceived fairness of the procedures used to develop the model and the proper distribution of the outcomes of the use of the model are important considerations. Procedural justice deals with the perception of the impartiality and transparency of the rules used to make decisions (Leventhal, 1980). According to Le Menestrel and Van Wassenhove’s (2004) responsibility principle, the processes operations researchers use to develop models must be perceived to be fair by the stakeholders affected by the model. For example, models have been used to help find ways to maximize Not-Sufficient Funds (NSF) fees in all customer accounts regardless of whether the customer has overdraft protection (Apte, et al., 2004). Certainly, from the perspective of the customer with overdraft protection, the procedures in place at these banks are perceived as unfair. The model was designed only with the client in mind. To consider the fairness to stakeholders who are impacted by the system, a new model was proposed that creates a “win-win” situation for both the banks and their customers. The revised, more procedurally just model permits the bank to issue overdraft limits in order to maximize their NSF fees, but also protects the customer from extra merchant-imposed fees for returned checks. Judgments about justice must also consider the distribution of outcomes—both positive and negative. “Distributive justice relates to the perceived fairness of the allocation of outcomes and is assumed to reflect a concern for one’s material well-being” (Ashworth and Free, 2006, p.113). Contrary to procedural justice which pertains more to the development of the model itself, distributive justice principles are highly relevant to the model in use. Thus, when a particular model helps guide a decision that leads to an imbalance of costs distributed across various stakeholders impacted by the model, it will be perceived as distributively unfair. Zenios, Chertow, and Wein (2000) wrote a simulation model to solve the kidney transplant allocation dilemma facing health care facilities. The current regulating body overseeing the allocation of organs in the U.S. is the United Network of Organ Sharing (UNOS). Their model which utilizes a points system has been criticized, and it has been revised twice. “…this allocation policy 15
generates inequities… among certain demographic groups” (Chertow and Wein, 2000, p.549). Thus, the distribution of the benefit (transplant organs) is perceived as unfair. The new model employs a new dynamic index policy adjusted for life expectancy, which when used demonstrated an improvement in waiting time across all races, both genders, and all age groups. For the perception of fair treatment to be elicited from the individuals who are affected by a model, operations researchers must also demonstrate respect for the stakeholders of the model (Lind and Tyler, 1988). A third type of justice needs to be included in discussions about ethical OR modeling—interactional justice (Cropanzano, et al., 2002). Individuals evaluate the “quality of interpersonal treatment they receive during the enactment of organizational procedures” (Bies and Moag, 1986, p.44). In terms of the treatment of people, interactional justice is made up of two components—interpersonal and informational. The former involves evaluating how an individual is treated by an entity (Greenberg, 1993). Respect and dignity of the stakeholders of a model must be maintained. The latter component, informational justice, holds that individuals need to be informed as to the rationale behind the adopted procedures, or the outcomes sought after (Greenberg, 1993). “Documentation of a model should explain the model’s underlying reasoning, especially its performance measures…and its assumptions…” (Kleijnen, 2001, p. 224). At Ankor’s warehouse, order-picking response time in the storage facility was significantly reduced by the implementation of a model that suggested a more efficient system (Dekker, et al., 2004). As a result, 25% of the employees in the warehouse were let go because they were no longer needed. While the company saved $140,000 Euros with the improvements, a severe cost to the order pickers was overlooked. By providing stakeholders with the reasons for the development of a DSS, those individuals may perceive there to be a greater amount of justice. “Individuals who believe they are treated with respect and dignity will be more likely to consider themselves as more valued members of the group, and more likely to perceive higher levels of justice” (Lilly and Virick, 2003, p.441). 4.1.3 Ethics of the good and ethics of the right In his 1993 interview, C. W. Churchman claimed that the only thing not being emphasized sufficiently in management is ethics. “I want to see the improvement of the human condition” (van Gigch et al. 1997, p.742).
In this line of thinking, we contend also that 16
operations research may well incorporate ethics into its paradigm through the understanding of a morality that focuses on the quality of life in addition to a morality that defends human rights. When developing models, it is critical for operations researchers and designers to consider the long-range effects (both positive and negative) of their work. Socially responsible modelers should attempt to anticipate the impacts of their models on society. Thus, protection of individual stakeholder’s rights remains paramount. Nonetheless, when considering the potential outcomes of a decision, it is of grand importance that the reasoning process involves an examination of the quality of the stakeholders’ lives. The ethics of the good must be taken into account when constructing norms of behavior. Perceived ethically defensible behavior in terms of the honoring of moral principles—the ethics of the right— currently dominates operations research thinking (Gallo, 2004; Le Menestrel and Van Wassehoven, 2004). Obligations to honor certain stakeholder’s rights are generated by what is perceived as socially permissible (Brey, 2007). While this has some merit, stakeholder identification and the normative issues existing therein must also consider quality of life and an individual’s well-being. Ethical issues arising from the use of models should act “as a guide to human fulfillment” (Mele, 2003, p. 4), or “happiness” as John Stuart Mill would describe, and should not be limited to a set of norms put in place to resolve dilemmas associated with decision making models. This Nicomachean view of ethical reasoning is applicable to OR concerns and should provide guidance for resolving the inherent ethical issues in modeling, as it deals with the utility of the model itself. In this section we have presented the principles of responsible modeling that lie at the normative core of ethically sound operations research. It is important to note that in order for more socially responsible decisions to be made when assessing the impact of the model, all the principles need to be considered in the context of the stakeholders affected. “If we ever hope to improve the quality of our ethical decision making, we need a methodology that combines the weighted effects of all applicable ethical guidelines on the issue at hand” (Millet, 1998, p.1197). Next we explain how such principles can be realized in OR procedures or techniques. We discuss four processes that can be considered when evaluating a model: sensitivity analysis, parameter transparency, validation, and informational networks. 4.2 Processes 4.2.1 Sensitivity analysis 17
Since any OR model is necessarily built on a set of assumptions, there is a significant chance that one or more of these assumptions will not hold in the practical application of the model. Careful implementation of OR models, therefore, should include a thorough sensitivity analysis of the recommended solution to determine the range of parameter values under which the recommendation is still viable. Decision makers should understand how the strategy should be altered in response to the realization of various future conditions. This enables the decision maker to create contingency plans, which characterizes a more holistic solution to a particular problem. For example, Stoddard’s (2005) model for maximizing the royalties the U.S. Minerals Management Service receives for the country’s natural gas reserves incorporates some sensitivity analysis by including transportation costs that are dependent upon the ultimate destination of the gas. He does not, however, report any sensitivity analysis with respect to production volumes or various price values that are used in the model. It would be beneficial for the decision maker to know how the network flow would change as some of these parameters vary. 4.2.2 Parameter transparency In addition to sensitivity analysis, effective OR models should also exhibit parameter transparency; that is, model builders should provide the users of the model with accurate information about the origin of the parameter values. OR models are only as useful as the data input into them. In fact, changing the parameter values of a model can cause the same model to advocate two opposite decision strategies. Wong and Roederer (2006) illustrate this facet of OR modeling with a simple two-player game concerning the U.S.’s decision to attack Iraq in 2003. Just by changing a few of the parameter inputs, the same model can support the decision to attack as well as the decision not to attack. In an interview conducted during Willemain’s 1994 study, one of the expert modelers described an attack assessment project in which each of the Joint Chiefs of Staff provided relative probabilities of particular types of attacks. It turned out that each Chief placed the highest probability on the attack that would give his command the highest strategic importance. The decision maker would have done well to learn the source of those parameter estimates in order to recognize their potential, if not certain, bias. 4.2.3 Validation Many OR models applied in practice address large-scale problems whose complexity precludes the development of analytical solution methods. As a result, heuristic and 18
computational methods are used to generate effective solutions to the problem. These solution methods must be validated in order to support their application to general decision environments. Without validation, a modeler should not have much confidence that a given algorithm will provide good solutions for other model parameterizations. Most OR professionals are wellversed in model validation as evidenced by the high importance Willemain’s experts placed on validation and verification in an effective modeling process (1994, 1995). For example, Stoddard’s (2005) model for maximizing federal natural gas royalties was validated in an initial test project. Documentation of the model’s underlying reasoning, performance measures, and its assumptions is critical for decision support systems (Kleijnen, 2001); decision makers need to know the context of development to use the model correctly, like an instruction manual. Documentation is also important for fellow modelers to replicate or adapt the model (Wilson, 1997). Kleijnen (2001) discusses the ethical implications of validation and comments that validation seems to be “more articulated in the public domain” and when the risks are extreme, such as a nuclear power plant. Certainly, when the model represents a unique event, validation becomes exceedingly difficult to attain. 4.2.4 Informational networks The elegance of various methods of OR modeling, especially optimization methods, can cause modelers to forget that models are implemented in a dynamic decision environment that changes over time. Decision makers receive feedback about the results of their actions and may need to alter the strategy if the current trajectory has gone askew. Thus, the implementation of OR models necessarily interacts with an information network of feedback loops through time. This information network can be modeled using systems dynamics and/or dynamic game theory methodologies. Where possible, an effective implementation strategy for OR models in these circumstances should include contingency plans for various results captured by the feedback process through time. Another key attribute of informational networks is the potential feedback they provide on the utility of a model. The inclusion of stakeholders is seen explicitly in Midgley et al. (1998), who describe the inclusion of stakeholders through workshops during an operations research project involving housing solutions for older people. Midgley (1999) clarifies that these 19
workshops provided opportunities for dialogue on desired outcomes. In a larger, more formalized project, the disposal of plutonium in the US, feedback from the decision makers and involved parties part way through the model development “led to the identification of inconsistencies in data collection” and to a standardization of performance measures (Butler, et al., 2005, p.100). Feedback through iterations of model development also influenced policy development and international technology transfer (Butler, et al., 2005). Addressing both documentation and feedback mechanisms, Kirkwood, Slaven, & Maltz’s supply-chain reconfiguration model “provides an audit trail that shows the reasons for the conclusion” and “will prompt them (managers and analysts) to seek input from experienced people” (Kirkwood, et al., 2005, p. 470). This contributed to the improvement of the model itself and to greater utility for the stakeholders of the model. 4.3 Outcomes The purpose of modeling – to assist in decision making – by definition leads to outcomes: outcomes of the model the decision maker uses and outcomes from the decision made and implemented. Businesses are under increasing pressure to assess their performance in terms of three areas: financial (or economic), environmental, and social. Assessing performance with these three perspectives is not new and is incorporated in such concepts as the triple bottom line (Elkington, 1994, 1998), total responsibility management (Waddock, et al., 2002; Waddock and Bodwell, 2007), the Balanced Scorecard (Kaplan and Norton, 1996), social and environmental accounting, and sustainability performance measures. Even recently, Theys and Kinsch called for stakeholder co-operation “towards a common long-term sustainability goal” (2004, p. 485). Examining the financial, environmental, and social outcomes by definition takes into account stakeholder considerations, and as Wood writes, “a cycle is completed as the problems and issues that motivated research … are addressed again” (1991, p. 708). Outcomes are more than financial measures or the number of employees affected. Outcomes are “observable outcomes as they relate to the firm’s societal relationships” (Wood, 1991, p.708). In terms of corporate social responsibility, outcomes include social impact, policies, and programs. For responsible model use, we define outcomes as those observable changes resulting from the model use and subsequent decision making. At the institutional level, these observable outcomes can be public policy or programs, such as policies in the criminal 20
justice system (Blumstein, 2002). At the firm level, these observable changes can include policies, programs, and more quantifiable elements such as cash flow, amount of energy used, and number of jobs changed. 4.3.1 Financial outcomes Traditionally used in business analysis, financial outcomes reflect the funds available or used. Financial outcomes include aspects of resource allocation, such as management control, risk, availability of funds, and investment strategy. OR models usually use some type of quantifiable financial or resource measure such as cost, taxes, or amount of resource used. Stoddard’s (2005) model to maximize federal natural gas royalties clearly integrated the combined costs of transportation, administration, and processing as well as the dynamic natural gas market (Stoddard, 2005). From the discussion presented in the article, however, this model did not capture environmental costs by comparing the environmental impact from the commercial natural gas companies versus the federal agency processing and transporting the natural gas. The IBM supply chain reconfiguration model reported by Kirkwood et al. (2005) is an excellent example of a model that included financial and social outcomes. They developed performance measures to address product quality, customer responsiveness, and operating constraints such as sophistication, skills and support structure (Kirkwood, et al, 2005). 4.3.2 Environmental outcomes The impact of organizations on the natural environment has become increasingly important over the past four decades throughout the world. The natural environment can be considered a stakeholder, although pressure from the environment comes from other stakeholder groups on its behalf or with congruent interests (Driscoll and Starik, 2004). Arguments are made that the natural environment should be considered in a stakeholder framework for normative reasons as well as pragmatic reasons (Buchholz, 1993; Shrivastava, 1995; Srikantia and Bilimoria, 1997; Starik, 1995; Stead and Stead, 1997, 2000); by not considering impacts on the natural environment, managers increase the risk of negative environmental outcomes and therefore increased regulation, cost or customer backlash. Several environmental performance measures are available and finding increasing acceptance in the global business community, such as the Global Reporting Initiative (GRI) and the ISO 14000 environmental management standards. In the operations research field specifically, Hervani et al (2005) propose a green 21
supply chain management performance measurement (GSCM/PM) that incorporates environmental performance indicators and other measures of management, employees, costs, customers and other impacts. 4.3.3 Social outcomes The third set of outcomes, social, is not yet as easily defined as the financial outcomes are, or as focused on a single, albeit broad, stakeholder as environmental measures are. Social outcomes are the impacts on stakeholders and are not as easily or fully captured with financial measures, such as job change, job loss, or employment conditions for employees and product safety for consumers. Indeed, measures of social outcomes econometric models to goal reporting and accounting, and the social part of the triple bottom line. An example of the noticeable lack of consideration of the employee stakeholder group is the model for warehouse operations by Dekker et al. (2004). In this paper, one of the outcomes of the decision made with the model was “a reduction in the number of order pickers of more than 25 percent” (Dekker et al., 2004, p.303). The financial savings to the company by reducing its workforce is estimated at €140,000, but any evaluation of the social outcomes is not made. Upon closer inspection of the paper, it appears that the company may have made a decision based on social outcomes, in this case minimizing job change, by choosing the option which a small percentage more walking effort but was closer to its existing method (Dekker et al., 2004). This is not to say that jobs should be kept despite more efficient operational techniques, but that the impacts of the loss of employees should be considered in the decision making. The financial costs of layoffs or retraining can be included in the financial outcomes, and the potential impact on reputation and stakeholder relationships could be considered. Social performance metrics are gaining legitimacy throughout global business through voluntary reporting, certification initiatives and some national regulations. Other international voluntary standards include SA8000, which focuses on labor practices, ISO 26000, which is in development for social responsibility management, and the Organization for Economic Cooperation and Development (OECD) Guidelines for Multinational Enterprises. Reputation is also a social outcome that includes stakeholders’ perceptions of the firm, accurate or not. Wright et al (Wright et al., 2007) studied the impact of stakeholders’ perception on organizations that dealt with suppliers that had poor working conditions. After a stakeholder coalition engaged in 22
media activity to expose the conditions, decision makers had to reevaluate the original sourcing decisions. This often had a significant financial impact on the businesses. Using the stakeholder framework may also lead decision makers to creative ways to mitigate the negative outcomes of a decision. For example, if a decision is made to go with option ‘1’, but in the analysis the negative impacts to stakeholder ‘A’ are realized, then part of the implementation plan may be to have a monitoring program for those negative impacts and even a threshold at which the choices will be reassessed. In the US decision to dispose of radioactive plutonium, two of the four highest rated options were combined to meet the overall objectives – a hybrid strategy for implementation (Butler, et al., 2005). In terms of risk management, the stakeholder framework can help identify risks and possibly ways to mitigate or minimize the risk. 5 Discussion and implications This paper takes a significant step forward for the application of ethics in OR modeling. We provide a stakeholder approach within a SRM framework for responsible use of modeling in a decision making environment. We take a normative approach to ethics in OR by saying that ethics is important and decision makers and models should be ethical in the development and use of models; however, we have not said which principles or values are more important. Rather, we present several principles with which a model can be assessed, within its own contextual environment. Our framework is prescriptive in that we are saying decision makers can use our proposed stakeholder framework and SRM framework to assess and use the model. We go beyond the inclination that more “robust” models must be developed in order to be sensitive to a wider variety of ethical concerns for the stakeholders of the model. We agree with Kleijnen (2004) that ethics are implicit in a model’s purpose, which includes the consideration of stakeholders’ interests. However, “ethical models” cannot be achieved “within the model” alone. 5.1 Applying the SRM framework Butler et al.’s (2005) account of the responsible use of a model in the decision making process is an excellent example of using the stakeholder framework for analysis of model social performance and ultimately aiding ethical decision making. While not everyone may agree with the decision and related activities, this is an excellent illustration of the stakeholder framework. In a multi-attribute utility model (MAU, (Kenney and Raiffa, 1976)) for the disposal of 23
radioactive material, the decision makers took into account stakeholders such as nongovernmental organizations, people involved in the disposition of the waste material, people and the natural environment in areas of preparation, transport and disposal of the material, future generations, the general public, and the international political community (Butler, et al., 2005). The principles of responsible modeling were addressed, summarized by this charge: “perform a MAU analysis to create a transparent and defensible record of the decision process that could withstand scrutiny by various interest groups” (Butler, et al., 2005, p. 90). (See Dyer et al., 1998) for a detailed account.) The modeling team addressed the processes of sensitivity analysis and parameter transparency by consulting with various experts in specific stakeholder concerns such as safety and environmental impact. In addition to considering stakeholders in the processes, the decision makers appeared to minimize aider priority effect by expecting the modelers “to support and to inform the OFMD’s decision-making process but not to choose the best alternative” (Butler, et al., 2005, p.90, emphasis added). In addition the modelers incorporated “objective data and subjective assessments” (Butler, et al., 2005, p. 90), further reducing aider priority effects of overlooked or difficult-to-incorporate knowledge (Brown, 2005). Finally, all three categories of outcomes were dealt with by the modelers: financial, environmental, and social. A unique performance measurement incorporated financial measures, environmental impacts, and social measures such as health and safety, socioeconomic impact, and potential theft or terrorism. 5.2 Additional considerations Throughout application of the SRM framework, inclusion of stakeholder feedback and documentation are vital to convey the ethical dimensions of the modeling team to the decision maker and vice-versa. In his description of the evolution of traffic theory and development of traffic models, Gazis called on government’s management of operations research to “be governed by appropriate objectives managed by knowledgeable bureaucrats” (2002, p.76). By documenting the assumptions that went into the model and processes of development, the OR modeler can ‘educate’ the decision maker as to the objective and base information that went into the model, hopefully providing a more knowledgeable decision maker for fitting use and interpretation of the model. 24
A decision support system addressing both quantitative and qualitative factors can be cited at IBM. The company improved their supply-chain reconfiguration decisions by including a multi-attribute utility function for decision makers (Kirkwood, et al., 2005). While CSR relationship factors were not included in the DSS, the model allowed for a probability analysis that did not limit alternatives solely on the basis of expected utility, but also included risk profiles. Whether the outcomes are evaluated in terms of the financial, environmental or social impacts, decision makers will inevitably have to make value trade-offs. This is not to say that it is an either-or choice between honesty and safety for example, rather that each objective may not be optimized, and the decision maker will explicitly or implicitly use moral values in determining acceptable trade-offs. Unlike Keeney (2002), who concluded that one had to rely on one’s own values where “you are the final judge” (p. 936), we contend that using the proposed stakeholder framework provides better support as it relies on principled reasoning as opposed to relying on an individual’s morals. Therefore, the decision maker may be the final judge, but does so after (or in) ethical consideration of stakeholders, processes, and outcomes. We contend that using the proposed stakeholder framework contests a decision maker’s ‘normative myopia,’ a condition where the decision maker, purposely or not, does not consider the ethical dimensions in their decisions (Swanson, 1999). 6 Limitations and opportunities for future research One potential limitation of our ethical evaluative framework is the fact that it is not often possible to foresee and consider all possible outcomes of a decision (Simon, 1957). “The problem with focusing on a single objective is that the world is complex, and managers and directors are boundedly rational…” (Freeman, et al., 2004). In fact, one major criticism of models in general is the fact that underlying their development is the assumption that humans are perfectly rational. The “perfect rational man” paradigm influences modeling and leads to a false comfort with the decisions they advise (Rubenstein, 1998). While we advocate a concern for a great breadth of stakeholders for present and future generations when making decisions, it is vain to presume that all considerations have been made. In terms of the specific components of the model, certain other limitations must be acknowledged. The three main principles outlined in our framework help to identify some of the 25
major ethical issues facing modelers. By incorporating an awareness of these principles, operations researchers may recognize which stakeholder groups benefit from a modeling decision and those whose interests are neglected. Still, the principles are not moral absolutes, nor are they a comprehensive representation of the ethical principles that should be considered in any discussion about the ethical efficacy of a particular model. Moreover, these principles are culturally variable; thus, they are not necessarily perceived the same way universally. In addition to cultural variation of the components of the SRM framework, it is also dependent on the context of a particular situation. The context in which a model is implemented is likely to change rendering the model more or less effective. If modelers are to include the stakeholders of the model in their decision criteria for developing a model, then it must be acknowledged that the stakeholders themselves influence what information is available to operations researchers. An effective model will be a function of what information the stakeholders of the model provide. By incorporating the various stakeholder perspectives, modelers could possibly reduce their bounded rationality and make more informed decisions. Of course, different stakeholder groups are also motivated by different agendas and needs (Wood, 1991). Future research should examine ways to include a broad array of those perspectives. Echoing Kleijnen’s call for the development of ethical procedural codes for OR, we feel the principles and processes advocated in our framework need to be institutionalized through a standard professional code. However, due to contextual differences based on stakeholder involvement, a “one size fits all” code borrowed from European OR societies may not be sufficient for modelers. Future research should build on the principles and processes in this article to create a meaningful code which guides the development of models, but also informs users on how to make ethical decisions when implementing the models. No matter how sophisticated the professional code in place there remains a potential problem. While having a code represents a positive SRM outcome, there is no guarantee that the model in use will be viewed as ethical. The best intentions may indeed omit primary stakeholders’ interests. Thus, future research must explore ways to ensure that the relationship between the procedures being advocated by a code actually lead to desired social outcomes when the model is in use.
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Table 1 Modeling Social Performance Assessment Framework Principles of responsible modeling ‐ Duties and Rights ‐ Justice and Fairness ‐ Ethics of the good/Ethics of the Right Processes ‐ Sensitivity analysis ‐ Parameter transparency ‐ Validation ‐ Informational networks Outcomes ‐ Financial outcomes ‐ Environmental outcomes ‐ Social outcomes
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