references.bib

@article{axelrod1997a,
  title = {The dissemination of culture: a model with local convergence and global polarization},
  shorttitle = {The dissemination of culture},
  author = {Axelrod, Robert},
  date = {1997},
  journaltitle = {The Journal of Conflict Resolution},
  volume = {41},
  number = {2},
  eprint = {174371},
  eprinttype = {jstor},
  pages = {203--226},
  publisher = {Sage Publications, Inc.},
  issn = {0022-0027},
  url = {https://www.jstor.org/stable/174371},
  urldate = {2024-09-18},
  abstract = {Despite tendencies toward convergence, differences between individuals and groups continue to exist in beliefs, attitudes, and behavior. An agent-based adaptive model reveals the effects of a mechanism of convergent social influence. The actors are placed at fixed sites. The basic premise is that the more similar an actor is to a neighbor, the more likely that that actor will adopt one of the neighbor's traits. Unlike previous models of social influence or cultural change that treat features one at a time, the proposed model takes into account the interaction between different features. The model illustrates how local convergence can generate global polarization. Simulations show that the number of stable homogeneous regions decreases with the number of features, increases with the number of alternative traits per feature, decreases with the range of interaction, and (most surprisingly) decreases when the geographic territory grows beyond a certain size.},
  langid = {english},
  keywords = {agent-based modeling,agent-based models,applied social sciences,complexity science,computational social science,cultural evolution,culture,interdisciplinary fields,modeling},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Axelrod - 1997 - The dissemination of culture a model with local convergence and global polarization.pdf}
}

@article{ballot2000,
  title = {Introduction: why simulation in social sciences?},
  shorttitle = {Introduction},
  author = {Ballot, Gérard and Weisbuch, Gérard},
  date = {2000-01},
  journaltitle = {Advances in Complex Systems},
  shortjournal = {Advs. Complex Syst.},
  volume = {3},
  pages = {9--16},
  issn = {0219-5259, 1793-6802},
  doi = {10.1142/S0219525900000029},
  url = {https://www.worldscientific.com/doi/abs/10.1142/S0219525900000029},
  urldate = {2024-08-05},
  issue = {01n04},
  langid = {english},
  keywords = {agent-based modeling,applied social sciences,complexity science,computational social science,computer simulations,interdisciplinary fields},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Ballot - 2000 - Introduction.pdf}
}

@incollection{box1979,
  title = {Robustness in the strategy of scientific model building},
  booktitle = {Robustness in statistics},
  author = {Box, George E. P.},
  editor = {Launer, Robert L. and Wilkinson, Graham N.},
  date = {1979},
  pages = {201--236},
  publisher = {Academic Press},
  doi = {10.1016/B978-0-12-438150-6.50018-2},
  url = {https://www.sciencedirect.com/science/article/pii/B9780124381506500182},
  urldate = {2024-07-09},
  abstract = {Robustness may be defined as the property of a procedure which renders the answers it gives insensitive to departures, of a kind which occur in practice, from ideal assumptions. Since assumptions imply some kind of scientific model, I believe that it is necessary to look at the process of scientific modelling itself to understand the nature of and the need for robust procedures. Against such a view it might be urged that some useful robust procedures have been derived empirically without an explicitly stated model. However, an empirical procedure implies some unstated model and there is often great virtue in bringing into the open the kind of assumptions that lead to useful methods. The need for robust methods seems to be intimately mixed up with the need for simple models. This we now discuss.},
  isbn = {978-0-12-438150-6},
  langid = {english},
  keywords = {exact sciences,modeling,probability and statistics,science},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Box - 1979 - Robustness in the strategy of scientific model building.pdf}
}

@book{epstein1996,
  title = {Growing artificial societies: social science from the bottom up},
  shorttitle = {Growing artificial societies},
  author = {Epstein, Joshua M. and Axtell, Robert},
  date = {1996},
  series = {Complex adaptive systems},
  publisher = {The Brookings Institution},
  location = {Washington, DC},
  abstract = {How do social structures and group behaviors arise from the interaction of individuals? Growing Artificial Societies approaches this question with cutting-edge computer simulation techniques. Fundamental collective behaviors such as group formation, cultural transmission, combat, and trade are seen to "emerge" from the interaction of individual agents following a few simple rules. In their program, named Sugarscape, Epstein and Axtell begin the development of a "bottom up" social science that is capturing the attention of researchers and commentators alike. The study is part of the 2050 Project, a joint venture of the Santa Fe Institute, the World Resources Institute, and the Brookings Institution. The project is an international effort to identify conditions for a sustainable global system in the next century and to design policies to help achieve such a system.},
  isbn = {978-0-262-05053-1 978-0-262-55025-3 978-0-262-55026-0},
  langid = {english},
  pagetotal = {208},
  keywords = {agent-based modeling,complexity science,fundamentals of agent-based modeling,modeling,social sciences},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Epstein and Axtell - 1996 - Growing artificial societies social science from the bottom up.pdf}
}

@article{epstein1999,
  title = {Agent-based computational models and generative social science},
  author = {Epstein, Joshua M.},
  date = {1999},
  journaltitle = {Complexity},
  volume = {4},
  number = {5},
  pages = {41--60},
  doi = {10.1002/(SICI)1099-0526(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F},
  abstract = {This article argues that the agent-based computational model permits a distinctive approach to social science for which the term "generative" is suitable. In defending this terminology, features distinguishing the approach from both "inductive" and "deductive" science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling pro- vides a powerful way to address certain enduring-and especially interdisciplinary questions. It allows one to subject certain core theories-such as neoclassical microeconomics-to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise-or "map up"-to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based ("bottom up") models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theo- retical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agent- based approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science-some related to intractability, and some to undecidability proper. Finally, since "emer- gence" figures prominently in this literature, I take up the connection between agent-based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible.},
  langid = {english},
  keywords = {agent-based modeling,applied social sciences,complexity science,computational social science,computer science,computer simulations,exact sciences,interdisciplinary fields,modeling,social sciences},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Epstein - 1999 - Agent-based computational models and generative social science.pdf}
}

@book{epstein2006,
  title = {Generative social science: studies in agent-based computational modeling},
  shorttitle = {Generative social science},
  author = {Epstein, Joshua M.},
  date = {2006},
  series = {Princeton studies in complexity},
  publisher = {Princeton University Press},
  location = {Princeton, NJ},
  abstract = {Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one “grows” the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein’s interdisciplinary research activities in the decade since the publication of his and Robert Axtell’s landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.},
  isbn = {978-0-691-12547-3},
  langid = {english},
  pagetotal = {356},
  keywords = {agent-based modeling,applied social sciences,complexity science,computer simulations,generative science,mathematical models,science,social sciences},
  annotation = {OCLC: ocm63680041},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Epstein - 2006 - Generative social science.epub;/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Epstein - 2006 - Generative social science.pdf}
}

@article{garcia2018,
  title = {Exploring the emergence and evolution of population patterns of leisure-time physical activity through agent-based modelling},
  author = {Garcia, Leandro M. T. and Diez Roux, Ana V. and Martins, André C. R. and Yang, Yong and Florindo, Alex A.},
  date = {2018-11-19},
  journaltitle = {International Journal of Behavioral Nutrition and Physical Activity},
  shortjournal = {Int J Behav Nutr Phys Act},
  volume = {15},
  number = {1},
  pages = {112},
  issn = {1479-5868},
  doi = {10.1186/s12966-018-0750-9},
  url = {https://doi.org/10.1186/s12966-018-0750-9},
  urldate = {2024-08-21},
  abstract = {Most interventions aiming to promote leisure-time physical activity (LTPA) at population level showed small or null effects. Approaching the problem from a systems science perspective may shed light on the reasons for these results. We developed an agent-based model to explore how the interactions between psychological attributes and built and social environments may lead to the emergence and evolution of LTPA patterns among adults.},
  langid = {english},
  keywords = {agent-based modeling,agent-based models,complexity science,computer simulations,health sciences,interdisciplinary fields,physical activity,physical education,theoretical models},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Garcia - 2018 - Exploring the emergence and evolution of population patterns of leisure-time.pdf;/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Garcia - 2018 - Exploring the emergence and evolution of population patterns of leisure-time.zip}
}

@article{gilbert2000,
  title = {How to build and use agent-based models in social science},
  author = {Gilbert, Nigel and Terna, Pietro},
  date = {2000-03-01},
  journaltitle = {Mind \& Society},
  shortjournal = {Mind \& Society},
  volume = {1},
  number = {1},
  pages = {57--72},
  issn = {1860-1839},
  doi = {10.1007/BF02512229},
  url = {https://doi.org/10.1007/BF02512229},
  urldate = {2024-09-12},
  abstract = {The use of computer simulation for building theoretical models in social science is introduced. It is proposed that agent-based models have potential as a “third way” of carrying out social science, in addition to argumentation and formalisation. With computer simulations, in contrast to other methods, it is possible to formalise complex theories about processes, carry out experiments and observe the occurrence of emergence. Some suggestions are offered about techniques for building agent-based models and for debugging them. A scheme for structuring a simulation program into agents, the environment and other parts for modifying and observing the agents is described. The article concludes with some references to modelling tools helpful for building computer simulations.},
  langid = {english},
  keywords = {agent based computational economics,agent-based modeling,artificial intelligence,classifier systems,complexity science,genetic algorithms,neural networks,social simulation},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Gilbert and Terna - 2000 - How to build and use agent-based models in social science.pdf}
}

@article{grimm2005a,
  title = {Pattern-oriented modeling of agent-based complex systems: lessons from ecology},
  shorttitle = {Pattern-oriented modeling of agent-based complex systems},
  author = {Grimm, Volker and Revilla, Eloy and Berger, Uta and Jeltsch, Florian and Mooij, Wolf M. and Railsback, Steven F. and Thulke, Hans-Hermann and Weiner, Jacob and Wiegand, Thorsten and DeAngelis, Donald L.},
  date = {2005-11-11},
  journaltitle = {Science},
  volume = {310},
  number = {5750},
  pages = {987--991},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/science.1116681},
  url = {https://www.science.org/doi/10.1126/science.1116681},
  urldate = {2024-09-24},
  abstract = {Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,computer science,interdisciplinary fields,modeling,pattern-oriented modeling},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm et al. - 2005 - Pattern-oriented modeling of agent-based complex systems lessons from ecology.pdf}
}

@book{grimm2005b,
  title = {Individual-based modeling and ecology},
  author = {Grimm, Volker and Railsback, Steven F.},
  editor = {Levin, Simon A.},
  editortype = {redactor},
  date = {2005},
  series = {Princeton {{Series}} in {{Theoretical}} and {{Computational Biology}}},
  publisher = {Princeton University Press},
  location = {Princeton, NJ},
  abstract = {Individual-based models are an exciting and widely used new tool for ecology. These computational models allow scientists to explore the mechanisms through which population and ecosystem ecology arises from how individuals interact with each other and their environment. This book provides the first in-depth treatment of individual-based modeling and its use to develop theoretical understanding of how ecological systems work, an approach the authors call “individual-based ecology.? Grimm and Railsback start with a general primer on modeling: how to design models that are as simple as possible while still allowing specific problems to be solved, and how to move efficiently through a cycle of pattern-oriented model design, implementation, and analysis. Next, they address the problems of theory and conceptual framework for individual-based ecology: What is “theory”? That is, how do we develop reusable models of how system dynamics arise from characteristics of individuals? What conceptual framework do we use when the classical differential equation framework no longer applies? An extensive review illustrates the ecological problems that have been addressed with individual-based models. The authors then identify how the mechanics of building and using individual-based models differ from those of traditional science, and provide guidance on formulating, programming, and analyzing models. This book will be helpful to ecologists interested in modeling, and to other scientists interested in agent-based modeling.},
  isbn = {0-691-09665-1},
  langid = {english},
  pagetotal = {448},
  keywords = {agent-based modeling,biological sciences,complexity science,computer science,computer simulations,ecology,exact sciences,individual-based modeling,interdisciplinary fields,mathematical models,modeling,odd protocol,population dynamics},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm - 2005 - Individual-based modeling and ecology.pdf}
}

@article{grimm2006a,
  title = {A standard protocol for describing individual-based and agent-based models},
  author = {Grimm, Volker and Berger, Uta and Bastiansen, Finn and Eliassen, Sigrunn and Ginot, Vincent and Giske, Jarl and Goss-Custard, John and Grand, Tamara and Heinz, Simone K. and Huse, Geir and Huth, Andreas and Jepsen, Jane U. and Jørgensen, Christian and Mooij, Wolf M. and Müller, Birgit and Pe’er, Guy and Piou, Cyril and Railsback, Steven F. and Robbins, Andrew M. and Robbins, Martha M. and Rossmanith, Eva and Rüger, Nadja and Strand, Espen and Souissi, Sami and Stillman, Richard A. and Vabø, Rune and Visser, Ute and DeAngelis, Donald L.},
  date = {2006-09-15},
  journaltitle = {Ecological Modelling},
  shortjournal = {Ecological Modelling},
  volume = {198},
  number = {1},
  pages = {115--126},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2006.04.023},
  url = {https://www.sciencedirect.com/science/article/pii/S0304380006002043},
  urldate = {2024-05-17},
  abstract = {Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,interdisciplinary fields,odd protocol,open science,protocols,standards},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm - 2006 - A standard protocol for describing individual-based and agent-based models.pdf}
}

@article{grimm2010,
  title = {The {{ODD}} protocol: a review and first update},
  shorttitle = {The {{ODD}} protocol},
  author = {Grimm, Volker and Berger, Uta and DeAngelis, Donald L. and Polhill, J. Gary and Giske, Jarl and Railsback, Steven F.},
  date = {2010-11-24},
  journaltitle = {Ecological Modelling},
  shortjournal = {Ecological Modelling},
  volume = {221},
  number = {23},
  pages = {2760--2768},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2010.08.019},
  url = {https://www.sciencedirect.com/science/article/pii/S030438001000414X},
  urldate = {2024-05-17},
  abstract = {The ‘ODD’ (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,interdisciplinary fields,odd protocol,open science,protocols,standards},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm - 2010 - The ODD protocol.pdf}
}

@article{grimm2020,
  title = {The {{ODD}} protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism},
  shorttitle = {The {{ODD}} protocol for describing agent-based and other simulation models},
  author = {Grimm, Volker and Railsback, Steven F. and Vincenot, Christian E. and Berger, Uta and Gallagher, Cara and DeAngelis, Donald L. and Edmonds, Bruce and Ge, Jiaqi and Giske, Jarl and Groeneveld, Jürgen and Johnston, Alice S. A. and Milles, Alexander and Nabe-Nielsen, Jacob and Polhill, J. Gareth and Radchuk, Viktoriia and Rohwäder, Marie-Sophie and Stillman, Richard A. and Thiele, Jan C. and Ayllón, Daniel},
  date = {2020},
  journaltitle = {Journal of Artificial Societies and Social Simulation},
  shortjournal = {JASSS},
  volume = {23},
  number = {2},
  pages = {7},
  issn = {1460-7425},
  doi = {10.18564/jasss.4259},
  url = {https://jasss.soc.surrey.ac.uk/23/2/7.html},
  langid = {english},
  keywords = {agent-based modeling,complexity science,interdisciplinary fields,modeling,odd protocol,open science,standards},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm - 2020 - The ODD protocol for describing agent-based and other simulation models.pdf;/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm - 2020 - The ODD protocol for describing agent-based and other simulation models.zip}
}

@article{grimm2020a,
  title = {The {{ODD}} protocol: an update with guidance to support wider and more consistent use},
  shorttitle = {The {{ODD}} protocol},
  author = {Grimm, Volker},
  date = {2020-07-15},
  journaltitle = {Ecological Modelling},
  shortjournal = {Ecological Modelling},
  volume = {428},
  pages = {109105},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2020.109105},
  url = {https://www.sciencedirect.com/science/article/pii/S0304380020301770},
  urldate = {2024-05-17},
  langid = {english},
  keywords = {agent-based modeling,complexity science,interdisciplinary fields,odd protocol,open science,protocols,standards},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm - 2020 - The ODD protocol.pdf}
}

@article{grimm2025,
  title = {Using the {{ODD}} protocol and {{NetLogo}} to replicate agent-based models},
  author = {Grimm, Volker and Berger, Uta and Calabrese, Justin M. and Cortés-Avizanda, Ainara and Ferrer, Jordi and Franz, Mathias and Groeneveld, Jürgen and Hartig, Florian and Jakoby, Oliver and Jovani, Roger and Kramer-Schadt, Stephanie and Münkemüller, Tamara and Piou, Cyril and Premo, L. S. and Pütz, Sandro and Quintaine, Thomas and Rademacher, Christine and Rüger, Nadja and Schmolke, Amelie and Thiele, Jan C. and Touza, Julia and Railsback, Steven F.},
  date = {2025-02},
  journaltitle = {Ecological Modelling},
  shortjournal = {Ecological Modelling},
  volume = {501},
  pages = {110967},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2024.110967},
  url = {https://www.sciencedirect.com/science/article/pii/S0304380024003557},
  urldate = {2024-12-29},
  abstract = {Replicating existing models and their key results not only adds credibility to the original work, it also allows modellers to start model development from an existing approach rather than from scratch. New theory can then be developed by changing the assumptions or scenarios tested, or by carrying out more in-depth analysis of the model. However, model replication can be challenging if the original model description is incomplete or ambiguous. Here we show that the use of standards can facilitate and speed up replication: the ODD protocol for describing models, and NetLogo, an easy-to-learn but powerful software platform and language for implementing agent-based models. To demonstrate the benefits of this approach, we conducted a replication experiment on 18 agent-based models from different disciplines. The researchers doing the replications had no or little previous experience using ODD and NetLogo. Their task was to rewrite the original model description using ODD, implement the model in NetLogo and try to replicate at least one exemplary main result. They were also asked to produce, if time allowed, some initial new results with the replicated model, and to record the total time spent on the replication exercise. Replication was successful for 15 out of 18 models. The time taken varied between 2 and 12 days, with an average of 5 days. ODD helped to systematically scan the original model description, while NetLogo proved easy and quick to learn, but difficult to debug when implementation problems arose. Although most of the models replicated were relatively simple, we conclude that even for more complex models it can be useful to use ODD and NetLogo for replication, at least for developing a prototype to help decide how to proceed with the replicated model. Overall, the use of both, standard approaches such as ODD and easy to learn but powerful software such as NetLogo, can promote coherence and efficiency within and between different models and modelling communities. Imagine if all modellers spoke ODD and NetLogo as a common language or lingua franca.},
  langid = {english},
  keywords = {agent-based models,complexity science,model replication,modeling,netlogo,odd protocol,standards,theory development},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Grimm et al. - 2025 - Using the ODD protocol and NetLogo to replicate agent-based models.pdf}
}

@book{janssen2020,
  title = {Introduction to agent-based modeling: with applications to social ecological, and social-ecological systems},
  author = {Janssen, Marco A.},
  date = {2020-01-03},
  abstract = {This textbook provides a practical introduction to agent-based modeling using NetLogo based on 15 years of teaching this methodology to students in the life and social sciences. Agent-based modeling is increasingly used by social and life scientists in their research and teaching. Agent-based modeling is discussed in this book as a research tool in tandem with other methodologies, as such attention is given to modeling as a scientific method. The book first describes basic concepts and introduces you to NetLogo. Then gets you more familiar with modeling and NetLogo by exploring several classic agent-based models. Further, we discuss methods to analyze models using sensitivity analysis and calibration. We close with a series of chapters on ecosystem management, and diffusion of viruses, and gossip and innovations in networks. Each chapter uses practical examples with NetLogo models, and those models are freely available to the readers.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,computational social science,fundamentals of agent-based modeling,modeling,social-ecological systems},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Janssen - 2020 - Introduction to agent-based modeling with applications to social ecological, and social-ecological.epub;/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Janssen - 2020 - Introduction to agent-based modeling with applications to social ecological, and social-ecological.pdf}
}

@article{meier2025,
  title = {Model perpetuation by designing and documenting models and workflows so that they can be reused and further developed by others: {{The}} case of multiple stressors in ecology},
  shorttitle = {Model perpetuation by designing and documenting models and workflows so that they can be reused and further developed by others},
  author = {Meier, Laura and Grimm, Volker and Frank, Karin},
  date = {2025-02-01},
  journaltitle = {Ecological Modelling},
  shortjournal = {Ecological Modelling},
  volume = {501},
  pages = {111029},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2025.111029},
  url = {https://www.sciencedirect.com/science/article/pii/S0304380025000122},
  urldate = {2025-02-07},
  abstract = {For model development and use, there are recommendations for documenting the model itself, the simulation experiments, or the whole modelling process in general, all of which contribute to good modelling practice (GMP). However, it remains a challenge to prepare models for their perpetuation, so that both the original developers and others can run them for new scenarios or develop the model further. As a result, despite the often considerable effort that goes into developing a model, it is not used any more as soon as the developers no longer have the resources to do so. We therefore present recommendations for Model Perpetuation, referred to as DOSE, which consists of four components: How to (1) Design, (2) Operationalize scenarios, (3) Simulate and (4) Evaluate. We focus on models that represent the effects and interactions of multiple stressors, as this type of model is becoming increasingly important in ecology and elsewhere. Our recommendations are based on the development of our mechanistic model of riverine ecosystems, MASTIFF. DOSE is intended as a checklist to facilitate Model Perpetuation and can therefore contribute to the development of a more comprehensive GMP. DOSE has the potential to increase the return on investment in model development. It can facilitate community model development, thereby broadening the scope of models and providing a much-needed stronger focus on multiple stressors. DOSE could be the first step towards a standardized approach to ensuring Model Perpetuation.},
  langid = {english},
  keywords = {agent-based models,complexity science,ecology,model perpetuation,modeling,reuse of models,standardization,stressors,workflows},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Meier et al. - 2025 - Model perpetuation by designing and documenting models and workflows so that they can be reused and.pdf}
}

@article{muller2013,
  title = {Describing human decisions in agent-based models – {{ODD}}~+~{{D}}, an extension of the {{ODD}} protocol},
  author = {Müller, Birgit and Bohn, Friedrich and Dreßler, Gunnar and Groeneveld, Jürgen and Klassert, Christian and Martin, Romina and Schlüter, Maja and Schulze, Jule and Weise, Hanna and Schwarz, Nina},
  date = {2013-10-01},
  journaltitle = {Environmental Modelling \& Software},
  shortjournal = {Environmental Modelling \& Software},
  volume = {48},
  pages = {37--48},
  issn = {1364-8152},
  doi = {10.1016/j.envsoft.2013.06.003},
  url = {https://www.sciencedirect.com/science/article/pii/S1364815213001394},
  urldate = {2024-05-17},
  abstract = {Representing human decisions is of fundamental importance in agent-based models. However, the rationale for choosing a particular human decision model is often not sufficiently empirically or theoretically substantiated in the model documentation. Furthermore, it is difficult to compare models because the model descriptions are often incomplete, not transparent and difficult to understand. Therefore, we expand and refine the ‘ODD’ (Overview, Design Concepts and Details) protocol to establish a standard for describing ABMs that includes human decision-making (ODD~+~D). Because the ODD protocol originates mainly from an ecological perspective, some adaptations are necessary to better capture human decision-making. We extended and rearranged the design concepts and related guiding questions to differentiate and describe decision-making, adaptation and learning of the agents in a comprehensive and clearly structured way. The ODD~+~D protocol also incorporates a section on ‘Theoretical and Empirical Background’ to encourage model designs and model assumptions that are more closely related to theory. The application of the ODD~+~D protocol is illustrated with a description of a social–ecological ABM on water use. Although the ODD~+~D protocol was developed on the basis of example implementations within the socio-ecological scientific community, we believe that the ODD~+~D protocol may prove helpful for describing ABMs in general when human decisions are included.},
  keywords = {agent-based modeling,complexity science,interdisciplinary fields,odd protocol,open science,protocols,standards},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Muller - 2013 - Describing human decisions in agent-based models – ODD + D, an extension of the.pdf;/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Muller - 2013 - Describing human decisions in agent-based models – ODD + D, an extension of the.zip}
}

@book{railsback2019a,
  title = {Agent-based and individual-based modeling: a practical introduction},
  shorttitle = {Agent-based and individual-based modeling},
  author = {Railsback, Steven F. and Grimm, Volker},
  date = {2019},
  edition = {2},
  publisher = {Princeton University Press},
  location = {Princeton, NJ},
  isbn = {978-0-691-19082-2},
  langid = {english},
  pagetotal = {340},
  keywords = {agent-based modeling,complexity science,exact sciences,fundamentals of agent-based modeling,interdisciplinary fields,modeling,odd protocol,probability and statistics,textbooks},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Railsback - 2019 - Agent-based and individual-based modeling.pdf}
}

@article{schelling1971,
  title = {Dynamic models of segregation},
  author = {Schelling, Thomas C.},
  date = {1971-07-01},
  journaltitle = {The Journal of Mathematical Sociology},
  volume = {1},
  number = {2},
  pages = {143--186},
  publisher = {Routledge},
  issn = {0022-250X},
  doi = {10.1080/0022250X.1971.9989794},
  url = {https://doi.org/10.1080/0022250X.1971.9989794},
  urldate = {2024-09-18},
  abstract = {Some segregation results from the practices of organizations, some from specialized communication systems, some from correlation with a variable that is non‐random; and some results from the interplay of individual choices. This is an abstract study of the interactive dynamics of discriminatory individual choices. One model is a simulation in which individual members of two recognizable groups distribute themselves in neighborhoods defined by reference to their own locations. A second model is analytic and deals with compartmented space. A final section applies the analytics to ‘neighborhood tipping.’ The systemic effects are found to be overwhelming: there is no simple correspondence of individual incentive to collective results. Exaggerated separation and patterning result from the dynamics of movement. Inferences about individual motives can usually not be drawn from aggregate patterns. Some unexpected phenomena, like density and vacancy, are generated. A general theory of ‘tipping’ begins to emerge.},
  langid = {english},
  keywords = {agent-based modeling,agent-based models,complexity science,extraordinary publications,game theory,segregation},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Schelling - 1971 - Dynamic models of segregation.pdf}
}

@book{smaldino2023,
  title = {Modeling social behavior: mathematical and agent-based models of social dynamics and cultural evolution},
  shorttitle = {Modeling social behavior},
  author = {Smaldino, Paul E.},
  date = {2023},
  publisher = {Princeton University Press},
  location = {Princeton},
  isbn = {978-0-691-22415-2},
  langid = {english},
  keywords = {agent-based modeling,applied social sciences,complexity science,cultural evolution,exact sciences,human sciences,interdisciplinary fields,mathematical models,psychology,social dynamics},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Smaldino - 2023 - Modeling social behavior.pdf}
}

@article{szangolies2024,
  title = {Visual {{ODD}}: a standardised visualisation illustrating the narrative of agent-based models},
  shorttitle = {Visual {{ODD}}},
  author = {Szangolies, Leonna and Rohwäder, Marie-Sophie and Ahmed, Hazem and Jahanmiri, Fatima and Wagner, Alexander and Souto-Veiga, Rodrigo and Grimm, Volker and Gallagher, Cara},
  date = {2024},
  journaltitle = {Journal of Artificial Societies and Social Simulation},
  shortjournal = {JASSS},
  volume = {27},
  number = {4},
  pages = {1},
  issn = {1460-7425},
  doi = {10.18564/jasss.5450},
  url = {https://www.jasss.org/27/4/1.html},
  abstract = {Agent-based models (ABMs) are commonly used tools across diverse disciplines, from ecology to social sciences and technology. Despite the effectiveness of the widely adopted Overview, Design concepts, and Details (ODD) protocol in ensuring transparency in ABM design and assumptions, the accompanying model descriptions are often lengthy, making quick overviews challenging. To facilitate comprehension, manuscripts, presentations, and posters often include visualisations of the model. Yet, the diversity of visualisation approaches complicates model comparisons and requires additional time for viewers to grasp the figure layouts. Additionally, these visualisations are usually poorly linked to corresponding sections of the written ODD model description. To address these challenges, we propose the standardised visual ODD (vODD) aimed to provide a quick overview of models and simplify the link to the written model description for readers who are more interested in specific elements. The standardised visualisation assigns defined positions for ODD elements for easy reference and comparison. We provide examples and guidance on constructing vODDs, along with templates for modellers to create their own visuals. While advocating for simplicity, we also illustrate how more complex models can still be effectively depicted in such visualisations. By establishing a generalised visualisation applicable to agent-based and other simulation models, we aim to improve the rapid comprehension of models and streamline graphical model representations in manuscripts, presentations, and posters.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,modeling,odd protocol,protocols},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Szangolies et al. - 2024 - Visual ODD a standardised visualisation illustrating the narrative of agent-based models.pdf}
}

@article{tisue2004,
  title = {{{NetLogo}}: a simple environment for modeling complexity},
  shorttitle = {{{NetLogo}}},
  author = {Tisue, Seth and Wilensky, Uri},
  date = {2004-05-16},
  journaltitle = {Center for Connected Learning and Computer-Based Modeling},
  url = {https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=a65e2af0f4a1b03db4b05357c4cb3b8a6a4d7894},
  abstract = {NetLogo is a multi-agent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper we focus on NetLogo as a tool for research and for teaching at the undergraduate level and higher. We outline the principles behind our design and describe recent and planned},
  langid = {english},
  keywords = {agent-based modeling,complexity science,computer science,netlogo},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Tisue and Wilensky - 2004 - NetLogo a simple environment for modeling complexity.pdf}
}

@article{wilensky2007,
  title = {Making models match: replicating an agent-based model},
  author = {Wilensky, Uri and Rand, William},
  date = {2007-10-31},
  journaltitle = {Journal of Artificial Societies and Social Simulation},
  shortjournal = {JASSS},
  volume = {10},
  number = {4},
  pages = {2},
  issn = {1460-7425},
  url = {https://www.jasss.org/10/4/2.html},
  abstract = {Scientists have increasingly employed computer models in their work. Recent years have seen a proliferation of agent-based models in the natural and social sciences. But with the exception of a few "classic" models, most of these models have never been replicated by anyone but the original developer. As replication is a critical component of the scientific method and a core practice of scientists, we argue herein for an increased practice of replication in the agent-based modeling community, and for widespread discussion of the issues surrounding replication. We begin by clarifying the concept of replication as it applies to ABM. Furthermore we argue that replication may have even greater benefits when applied to computational models than when applied to physical experiments. Replication of computational models affects model verification and validation and fosters shared understanding about modeling decisions. To facilitate replication, we must create standards for both how to replicate models and how to evaluate the replication. In this paper, we present a case study of our own attempt to replicate a classic agent-based model. We begin by describing an agent-based model from political science that was developed by Axelrod and Hammond. We then detail our effort to replicate that model and the challenges that arose in recreating the model and in determining if the replication was successful. We conclude this paper by discussing issues for (1) researchers attempting to replicate models and (2) researchers developing models in order to facilitate the replication of their results.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,ethnocentrism,exact sciences,interdisciplinary fields,modeling,replication,validation,verification},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Wilensky - 2007 - Making models match.pdf}
}

@article{wilensky2013,
  title = {Modeling nature’s emergent patterns with multi-agent languages},
  shorttitle = {{{NetLogo}}},
  author = {Wilensky, Uri},
  date = {2013},
  journaltitle = {Center for Connected Learning and Computer-Based Modeling},
  url = {https://ccl.northwestern.edu/2013/mnep9.pdf},
  abstract = {NetLogo is a multi-agent modeling language, a parallel extension of Logo. NetLogo is designed to enable scientists to conduct their research by building and analyzing agent-based models and for learners to explore and construct models of emergent phenomena. By exploring and constructing such models, students make connections between the micro-level of agents following rules and the macro-level patterns and regularities that constitute the world of natural and social phenomena.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,computer science,emergence,modeling,netlogo},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Wilensky - 2013 - Modeling nature’s emergent patterns with multi-agent languages.pdf}
}

@book{wilensky2015,
  title = {An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with {{NetLogo}}},
  shorttitle = {An introduction to agent-based modeling},
  author = {Wilensky, Uri and Rand, William},
  date = {2015-04-17},
  publisher = {The MIT Press},
  location = {Cambridge, MA},
  abstract = {A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples.The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—with hundreds of examples and exercises using NetLogo—enables readers to begin constructing models immediately, regardless of experience or discipline.The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.},
  isbn = {978-0-262-73189-8},
  langid = {english},
  pagetotal = {744},
  keywords = {agent-based modeling,complexity science,computer simulations,exact sciences,fundamentals of agent-based modeling,interdisciplinary fields,modeling,netlogo},
  annotation = {a},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Wilensky - 2015 - An introduction to agent-based modeling.pdf}
}

@article{zurell2010,
  title = {The virtual ecologist approach: {{Simulating}} data and observers},
  shorttitle = {The virtual ecologist approach},
  author = {Zurell, Damaris and Berger, Uta and Cabral, Juliano S. and Jeltsch, Florian and Meynard, Christine N. and Münkemüller, Tamara and Nehrbass, Nana and Pagel, Jörn and Reineking, Björn and Schröder, Boris and Grimm, Volker},
  date = {2010-04},
  journaltitle = {Oikos},
  volume = {119},
  number = {4},
  pages = {622--635},
  publisher = {John Wiley \& Sons, Ltd},
  issn = {0030-1299},
  doi = {10.1111/j.1600-0706.2009.18284.x},
  url = {https://nsojournals.onlinelibrary.wiley.com/doi/10.1111/j.1600-0706.2009.18284.x},
  urldate = {2025-02-19},
  abstract = {Ecologists carry a well-stocked toolbox with a great variety of sampling methods, statistical analyses and modelling tools, and new methods are constantly appearing. Evaluation and optimisation of these methods is crucial to guide methodological choices. Simulating error-free data or taking high-quality data to qualify methods is common practice. Here, we emphasise the methodology of the ?virtual ecologist? (VE) approach where simulated data and observer models are used to mimic real species and how they are ?virtually? observed. This virtual data is then subjected to statistical analyses and modelling, and the results are evaluated against the ?true? simulated data. The VE approach is an intuitive and powerful evaluation framework that allows a quality assessment of sampling protocols, analyses and modelling tools. It works under controlled conditions as well as under consideration of confounding factors such as animal movement and biased observer behaviour. In this review, we promote the approach as a rigorous research tool, and demonstrate its capabilities and practical relevance. We explore past uses of VE in different ecological research fields, where it mainly has been used to test and improve sampling regimes as well as for testing and comparing models, for example species distribution models. We discuss its benefits as well as potential limitations, and provide some practical considerations for designing VE studies. Finally, research fields are identified for which the approach could be useful in the future. We conclude that VE could foster the integration of theoretical and empirical work and stimulate work that goes far beyond sampling methods, leading to new questions, theories, and better mechanistic understanding of ecological systems.},
  langid = {english},
  keywords = {agent-based modeling,biological systems,ecology,modeling,simulations},
  file = {/home/danielvartan/Insync/danvartan@gmail.com/Google Drive/Zotero/files/Zurell et al. - 2010 - The virtual ecologist approach Simulating data and observers.pdf}
}