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Minding Morality: Ethical Artificial Societies for Public Policy Modeling Old Dominion University ODU Digital Commons VMASC Publications Virginia Modeling, Analysis & Simulation Center 2020 Minding Morality: Ethical Artificial Societies for Public olicyP Modeling Saikou Y. Diallo Old Dominion University, [email protected] F. LeRon Shults Wesley J. Wildman Follow this and additional works at: https://digitalcommons.odu.edu/vmasc_pubs Part of the Applied Ethics Commons, and the Digital Communications and Networking Commons Original Publication Citation Diallo, S.Y., Shults, F.L. & Wildman, W.J. Minding morality: ethical artificial societies for public policy modeling. AI & Soc (2020). https://doi.org/10.1007/s00146-020-01028-5 This Article is brought to you for free and open access by the Virginia Modeling, Analysis & Simulation Center at ODU Digital Commons. It has been accepted for inclusion in VMASC Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. AI & SOCIETY https://doi.org/10.1007/s00146-020-01028-5 ORIGINAL ARTICLE Minding morality: ethical artifcial societies for public policy modeling Saikou Y. Diallo1 · F. LeRon Shults2 E) · Wesley J. Wildman3 E) Received: 12 December 2019 / Accepted: 22 July 2020 © The Author(s) 2020 Abstract Public policies are designed to have an impact on particular societies, yet policy-oriented computer models and simulations often focus more on articulating the policies to be applied than on realistically rendering the cultural dynamics of the target society. This approach can lead to policy assessments that ignore crucial social contextual factors. For example, by leaving out distinctive moral and normative dimensions of cultural contexts in artifcial societies, estimations of downstream policy efectiveness fail to account for dynamics that are fundamental in human life and central to many public policy challenges. In this paper, we supply evidence that incorporating morally salient dimensions of a culture is critically important for produc- ing relevant and accurate evaluations of social policy when using multi-agent artifcial intelligence models and simulations. Keywords Multi-agent artifcial intelligence · Social simulation · Public policy · Ethics · Morality · Cultural norms 1 Introduction aim to strike the balance between complexity and abstraction in such a way as to optimize accuracy in projections related Testing complex policies in the real world is difcult due to the specifc policy in question (Edmonds and Moss 2004; to ethical considerations, cost of evaluation, and challenges Jager 2017). But it is difcult to generate concrete estimates in generalizing test outcomes. It is understandable, there- of the price paid for a decision to abstract from any given fore, that policy professionals would turn to computational dimension of cultural life. We contend that including spe- policy modeling as an ethical and afordable way of gen- cifc morally laden aspects of a society (e.g. marriage rituals, erating cost–beneft estimates of policy proposals before patterns of interpersonal contacts, behavioral prohibitions) they are implemented. To keep such models manageable can drastically alter the estimated impact of a policy, which and affordable, policy modelers naturally seek to make implies that policy models lacking those distinctive moral reasonable simplifcations of formidably intricate cultural features are limited in their relevance and accuracy. As a contexts, knowing that there is always a price to be paid for guide to future work in policy simulation, we identify base- such simplifcations and abstractions. Good policy models line aspects of an artifcial society that are needed to provide more accurate evaluations of the impact of almost any social policy in a complex social system. * F. LeRon Shults It is important to note that our attempt to overcome this [email protected] particular limitation in most current approaches to public Saikou Y. Diallo policy modeling (the failure to mind morality) is not meant [email protected] to obscure the many other limitations that are inherent to Wesley J. Wildman this methodology. As the common adage goes: “all models [email protected] are wrong, but some are useful.” The goal is to render one’s model as useful as possible while acknowledging the ways 1 Virginia Modeling, Simulation and Analysis Center, Old Dominion University, 1030 University Blvd., Sufolk, in which it is wrong, as well as its epistemological and her- VA 23435, USA meneutical limitations (Tolk et al. 2018; Tolk 2019). In this 2 Center for Modeling Social Systems at NORCE, and Institute context, our goal is not to defend the assumptions or validate for Global Development and Social Planning, The University the specifc outcomes of the particular simulation experi- of Agder, Universitetsveien 19, 4633 Kristiansand, Norway ments outlined below but to point out the extent to which 3 Center for Mind and Culture, and Boston University, 745 including (or failing to include) morally salient features Commonwealth Avenue, Boston, MA 02215, USA Published online: 07 August 2020 Vol.:(0123456789)1 3 AI & SOCIETY within an artifcial society impacts the policy relevance of dealing with the Big Problems of society. Developments in any of its outcomes. This argument will become increas- this feld, they argued, ingly important as simulation experiments within artifcial will make it possible to model and simulate social societies are used to address societal challenges such as the processes on a global scale, allowing us to take full COVID-19 pandemic (Squazzoni et al. 2020) and confict account of the long-distance interdependencies that exacerbated by climate change (Shults and Wildman 2020), characterise today’s heavily interconnected world. The the efects of which vary signifcantly across diverse cultural output of these simulations will be used to support contexts. policy makers in their decision making, to enable them to efciently and efectively identify optimal paths for 2 State of the art review our society. Similarly, open access to these large-scale simulations will support individuals in their evaluation of diferent policy options in the light of their per- The systems in relation to which public policies must be sonal needs and goals, greatly enhancing citizen par- proposed, analyzed, implemented, and evaluated are exceed- ticipation in this decision process. These developments ingly complex. All too often policy professionals are faced together open the doors to a much safer, more sustain- with “wicked problems” within these systems, in the sense able and fairer global society (Conte et al. 2012). that some “solutions” can cause unexpected perturbations that make things worse. As the human population and con- We have not yet passed through those doors. Despite the sumption of resources continue to grow, so does the urgency intense interest in promoting wide-scale use of computa- of the need to develop new ways to address this sort of prob- tional methodologies for modeling and simulating policy, lem (Cliquet and Avramov 2018). there has not yet been a breakthrough. As an example, consider Dengvaxia, which won FDA Nevertheless, computational social science is making approval in 2019 (US Food and Drug Administration 2019) progress. It has not gone unnoticed within the feld that as a vaccine to prevent recurrence of Dengue Fever in complexity science directly bears on policy considerations children aged 9–16 who had already been infected once. when it comes to developing realistic agent-based models Dengue Fever is the most prevalent mosquito-borne viral for social simulation. There are at least two diferent ways disease, directly afecting one-third of the world’s popula- that complexity theory can help: “First, it can help provide tion. Preventing a second infection is the key to avoiding representational models that might be used to constrain the the worst efects of the Dengue virus. Unfortunately, when range of strategies under consideration and, second, can help administered before prior infection, the vaccine acts like a inform second-order considerations concerning the ways frst infection and can make a second infection more dan- in which policy might be developed and/or adopted—the gerous. This was an unexpected result and led to the deaths policy adaptation process itself” (Jager and Edmonds 2015, of a small number of children in the Philippines. For this 64). The productivity of linking social simulation and com- reason, the approved deployment of Dengvaxia after prior plexity science is also evidenced in many contributions to infection, despite all the good it is doing, also threw fuel on Simulating Social Complexity: A Handbook, which provides the fre of anti-vaccination sentiment. Anti-vaxxers routinely philosophical and methodological refection on the process cite the case, and this has directly contributed to millions as well as multiple examples (Edmonds and Meyer 2017). of parents refusing vaccines for their children even when Computational modeling based on research in complex- those vaccines are known to have extremely rare or no side ity science has been applied to a host of policy-relevant efects, leading in turn to new outbreaks of deadly infectious issues including global migration, intergroup confict, diseases. This is an unintended side efect of a well-intended strategies of counterinsurgency, and international devel- policy intervention with highly non-linear amplifcation in opment aid (Wilson 2016; Neumann 2014; Pechenkina and an unexpected direction. It is
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