1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; Toward inverse generative social science using multi-objective genetic programming 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Tuong Manh Vu School of Health and Related Research, University of Sheffield, 30 Regent Street, Sheffield, UK t.vu@sheffield.ac.uk (+44) 114 222 6397 Charlotte Probst Institute for Mental Health Policy Research, Centre for Addiction & Mental Health, 33 Russell Street, Toronto, Canada charlotte.probst@camh.ca (+1) 416 535 8501 Joshua M. Epstein New York University, 715/719 Broadway, New York, USA je65@nyu.edu (+1) 212 992 3702 Alan Brennan School of Health and Related Research, University of Sheffield, 30 Regent Street, Sheffield, UK a.brennan@sheffield.ac.uk (+44) 114 222 0684 Mark Strong School of Health and Related Research, University of Sheffield, 30 Regent Street, Sheffield, UK m.strong@sheffield.ac.uk (+44) 114 222 0812 Robin C. Purshouse Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, UK r.purshouse@sheffield.ac.uk (+44) 114 222 5618 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Tuong Manh Vu 4. the abstract of the paper(s); Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method – based on multi-objective genetic programming – for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; D, G 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); (D) Our work uses genetic programming (GP) to explore the space of micro-level behavioral rules of a mechanism-based social systems model to reproduce historically observed macro-level dynamics in the patterns of alcohol use in American society since the early 1980s. Human modelers used social norms theory to define concepts and mechanisms for the social systems model. These concepts and mechanisms were then used in a GP process to identify other possible models. Most models found by the GP are better than the human-generated model and we discuss the trade-off between model interpretability and accuracy. The result is publishable in its own right as a new scientific result independent of the fact that the result was mechanically created because this is the first explanatory model of long-run alcohol use dynamics existing in the literature. (G) Given the surge in popularity for systems-based approaches for helping resolve societal problems, social scientists working with complex social systems face a challenge of designing appropriate social mechanisms to explain the dynamics of the system. In particular, the empirical correlations between social concepts are often identified, but the social mechanisms driving these observations were not defined in a quantifiable way. Conceptual and detailed design of these mechanisms in a computational model is the task of the modelers and is considered a very challenging problem. Our work – which we call inverse generative social science – presents a promising solution to this problem of indisputable difficulty in the computational social science field. 7. a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Tuong Manh Vu, Charlotte Probst, Joshua M. Epstein, Alan Brennan, Mark Strong, Robin C. Purshouse (2019). Toward inverse generative social science using multi-objective genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19. Prague, Czech Republic. 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Any prize money, if any, is to be divided equally among the co-authors. 9. a statement stating why the authors expect that their entry would be the "best," and Inverse generative social science offers new possibilities for the development of empirically-grounded social theory. In this paper, we have developed a first formal process for model discovery that can underpin inverse generative efforts. Given the surge in popularity for systems-based approaches for helping resolve societal problems, social scientists can use our approach to systematically explore the “cogs and wheels of the internal machinery” of the social phenomena and gain a better understanding of the social world. Our methodology’s novel ambition is in combining multiple theories to enrich the chain of causal mechanisms to better explain the social system than any single theory could. 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. GP (genetic programming) 11. The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement. The work was submitted to GECCO '19 on 30 Jan 2019 and accepted on 21 Mar 2019. The final camera-ready version was submitted on 2 May 2019.