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Relevant bibliographies by topics / Opponent model

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Academic literature on the topic 'Opponent model'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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Journal articles on the topic "Opponent model"

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Davies, Ian, Zheng Tian, and Jun Wang. "Learning to Model Opponent Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.

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Multi-Agent Reinforcement Learning (MARL) considers settings in which a set of coexisting agents interact with one another and their environment. The adaptation and learning of other agents induces non-stationarity in the environment dynamics. This poses a great challenge for value function-based algorithms whose convergence usually relies on the assumption of a stationary environment. Policy search algorithms also struggle in multi-agent settings as the partial observability resulting from an opponent's actions not being known introduces high variance to policy training. Modelling an agent's

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Shen, Macheng, and Jonathan P. How. "Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.

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This paper studies decision-making in two-player scenarios where the type (e.g. adversary, neutral, or teammate) of the other agent (opponent) is uncertain to the decision-making agent (protagonist), which is an abstraction of security-domain applications. In these settings, the reward for the protagonist agent depends on the type of the opponent, but this is private information known only to the opponent itself, and thus hidden from the protagonist. In contrast, as is often the case, the type of the protagonist agent is assumed to be known to the opponent, and this information-asymmetry signi

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Li, Junkang, Bruno Zanuttini, and Véronique Ventos. "Opponent-Model Search in Games with Incomplete Information." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 9840–47. http://dx.doi.org/10.1609/aaai.v38i9.28844.

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Games with incomplete information are games that model situations where players do not have common knowledge about the game they play, e.g. card games such as poker or bridge. Opponent models can be of crucial importance for decision-making in such games. We propose algorithms for computing optimal and/or robust strategies in games with incomplete information, given various types of knowledge about opponent models. As an application, we describe a framework for reasoning about an opponent's reasoning in such games, where opponent models arise naturally.

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Jing, Yuheng, Kai Li, Bingyun Liu, et al. "An Open-Ended Learning Framework for Opponent Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23222–30. https://doi.org/10.1609/aaai.v39i22.34488.

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Opponent Modeling (OM) aims to enhance decision-making by modeling other agents in multi-agent environments. Existing works typically learn opponent models against a pre-designated fixed set of opponents during training. However, this will cause poor generalization when facing unknown opponents during testing, as previously unseen opponents can exhibit out-of-distribution (OOD) behaviors that the learned opponent models cannot handle. To tackle this problem, we introduce a novel Open-Ended Opponent Modeling (OEOM) framework, which continuously generates opponents with diverse strengths and sty

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Otto, Jacob, and William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action." International Studies Quarterly 65, no. 2 (2020): 500–511. http://dx.doi.org/10.1093/isq/sqaa081.

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Abstract When an actor catches a state taking an objectionable secret action, it faces a dilemma. Exposing the action could force unresolved states to terminate the behavior to save face. However, it could also provoke resolved states to double down on the activity now that others are aware of the infraction. We develop a model that captures this fundamental trade-off. Three main results emerge. First, the state and its opponent may engage in a form of collusion—opponents do not expose resolved states despite their distaste for the behavior. Second, when faced with uncertainty, the opponent ma

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Liu, Chanjuan, Jinmiao Cong, Tianhao Zhao, and Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling." Mathematics 11, no. 14 (2023): 3062. http://dx.doi.org/10.3390/math11143062.

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The payoff of an agent depends on both the environment and the actions of other agents. Thus, the ability to model and predict the strategies and behaviors of other agents in an interactive decision-making scenario is one of the core functionalities in intelligent systems. State-of-the-art methods for opponent modeling mainly use an explicit model of opponents’ actions, preferences, targets, etc., that the primary agent uses to make decisions. It is more important for an agent to increase its payoff than to accurately predict opponents’ behavior. Therefore, we propose a framework synchronizing

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Wang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang, and Zhongjie Zhang. "Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability." Applied Sciences 12, no. 14 (2022): 6953. http://dx.doi.org/10.3390/app12146953.

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In multi-agent domains, dealing with non-stationary opponents that change behaviors (policies) consistently over time is still a challenging problem, where an agent usually requires the ability to detect the opponent’s policy accurately and adopt the optimal response policy accordingly. Previous works commonly assume that the opponent’s observations and actions during online interactions are known, which can significantly limit their applications, especially in partially observable environments. This paper focuses on efficient policy detecting and reusing techniques against non-stationary oppo

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Donkers, H. "Probabilistic opponent-model search." Information Sciences 135, no. 3-4 (2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.

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Redden, Ralph S., Greg A. Gagliardi, Chad C. Williams, Cameron D. Hassall, and Olave E. Krigolson. "Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems." Games 12, no. 3 (2021): 62. http://dx.doi.org/10.3390/g12030062.

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When we play competitive games, the opponents that we face act as predictors of the outcome of the game. For instance, if you are an average chess player and you face a Grandmaster, you anticipate a loss. Framed in a reinforcement learning perspective, our opponents can be thought of as predictors of rewards and punishments. The present study investigates whether facing an opponent would be processed as a reward or punishment depending on the level of difficulty the opponent poses. Participants played Rock, Paper, Scissors against three computer opponents while electroencephalographic (EEG) da

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Donkers, H. "Admissibility in opponent-model search." Information Sciences 154, no. 3-4 (2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.

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Dissertations / Theses on the topic "Opponent model"

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Lau, Hoi Ying. "Neural inspired color constancy model based on double opponent neurons /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20LAU.

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Koerper, Jason A. "A new colour quality model for ultra-high efficiency light sources with discontinuous spectra." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112359/1/Jason_Koerper_Thesis.pdf.

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This thesis demonstrates a novel approach to assessing the colour rendition of objects by high efficacy light sources. It establishes a relationship between a light source's output and the visual system, and develops a procedure to evaluate the colour quality of a light source based its fundamental spectral properties. Key wavelengths that link colour rendering and the human colour vision system are identified within. Furthermore, a predictive colour quality model based on the fundamental properties of a light source is presented, which is significantly different to existing, reference-based c

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Mojalefa, M. J. (Mawatle Jeremiah) 1948. "Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)." Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.

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In this thesis, Sebilwane is the subject of a narratological investigation. The point of departure of this study is based on the fact that a narratological text consists of three levels: the history, the composition, the usage of words which are recognisable in the style of the author. The epic-poem is not the subject of a verse-technical investigation and description. The narratological model is adapted to the aim of this study. The historical level regarded in principle as the original level prior to the material's exposure to a viewpoint and it is interpreted. The four narrative elements th

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Sailer, Zbyněk. "Vyhledání podobných obrázků pomocí popisu barevným histogramem." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236514.

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This thesis deals with description of existing methods of image retrieval. It contains set of methods for image description, coding of global and local descriptor (SIFT, etc.) and describes method of effective searching in multidimensional space (LSH). It continues with proposal and testing of three global descriptors using color histograms, histogram of gradients and the combination of both. The last part deals with similar image retrieval using proposed descriptors and the indexing method LSH and compares the results with the existing method. Product of this work is an experimental applicati

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Li, Junkang. "Games with incomplete information : complexity, algorithmics, reasoning." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.

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Dans cette thèse, on étudie les jeux à information incomplète. On commence par établir un paysage complet de la complexité du calcul des stratégies pures optimales pour différentes sous-catégories de jeux, lorsque les jeux sont explicitement donnés en entrée. On étudie ensuite la complexité lorsque les jeux sont représentés de façon compacte (par leurs règles de jeu, par exemple). Pour ce faire, on conçoit deux formalismes pour ces représentations compactes. Dans la suite, on se consacre aux jeux à information incomplète, en proposant d'abord un nouveau formalisme, nommé jeu combinatoire à inf

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Hladky, Stephen Michael. "Predicting opponent locations in first-person shooter video games." Master's thesis, 2009. http://hdl.handle.net/10048/600.

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Thesis (M. Sc.)--University of Alberta, 2009.<br>Title from PDF file main screen (viewed on Oct. 2, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

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Books on the topic "Opponent model"

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K, Davis Paul. Thinking about opponent behavior in crisis and conflict: A generic model for analysis and group discussion. Rand, 1991.

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Graziano, William G., and Renée M. Tobin. Agreeableness and the Five Factor Model. Edited by Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.17.

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Agreeableness is a summary label for individual differences in the motivation to maintain positive relations with others. Agreeableness is one of the major dimensions in the Big Five structural model of personality. It is also a major domain in the Five Factor Model of personality. This chapter provides an overview of the considerable body of research concerning the conceptualization, assessment, and etiology of Agreeableness with a focus on its six facets. It concludes with a discussion of alternative theoretical explanations for Agreeableness. In particular, an opponent process model that in

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Laver, Michael, and Ernest Sergenti. Endogenous Parties, Interaction of Different Decision Rules. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0006.

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This chapter attempts to develop more realistic and interesting models in which the set of competing parties is a completely endogenous output of the process of party competition. It also seeks to model party competition when different party leaders use different decision rules in the same setting by building on an approach pioneered in a different context by Robert Axelrod. This involves long-running computer “tournaments” that allow investigation of the performance and “robustness” of decision rules in an environment where any politician using any rule may encounter an opponent using either

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Hoppe, Sherry, and Bruce W. Speck, eds. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.

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Although service-learning programs can have diverse theoretical roots, faculty who engage their students in service-learning may not be be cognizant of alternatives to the one they adopt. This book presents not only a historical perspective, but it also debates the theories and issues surrounding the conflicts inherent in those theories. One theory, based on a philanthropic model, engages students in a commitment to serve others from a sense of gratitude for their own good fortunes or from a desire to give back to communities from which they have benefited. Typically, service-learning programs

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Thompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.

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Montaigne and the Tolerance of Politics provides a new interpretation of Michel de Montaigne’s Essais in the context of his activity as a political negotiator between combatant parties during the French Wars of Religion. At the heart of the Essais lies a political conception of tolerance that is rarely considered today. Tolerance is usually conceived as an individual ethical disposition or a moral principle of public law. For Montaigne, tolerance is instead a political capacity: the power and ability to negotiate relationships of basic trust and civil peace with one’s opponents in political co

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Thompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.

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This chapter investigates Michel de Montaigne’s engagement with the Italian humanist conception of “civil conversation” as an exercise for training effective political counselors and ambassadors. Using himself as a model, Montaigne prescribes a more confrontational, uncivil form of conversation as a means to train his readers into a high tolerance for political negotiation with the widest possible range of interlocutors and opinions. Following the conventions of the humanist literature on political education, Montaigne argues that the best way to practice political negotiation and other forms

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Laver, Michael, and Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.

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Party competition for votes in free and fair elections involves complex interactions by multiple actors in political landscapes that are continuously evolving, yet classical theoretical approaches to the subject leave many important questions unanswered. This book offers the first comprehensive treatment of party competition using the computational techniques of agent-based modeling. This exciting new technology enables researchers to model competition between several different political parties for the support of voters with widely varying preferences on many different issues. The book models

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Garloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.

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Since the late eighteenth century, writers and thinkers have used the idea of love—often unrequited or impossible love—to comment on the changing cultural, social, and political position of Jews in the German-speaking countries. This book asks what it means for literature (and philosophy) to use love between individuals as a metaphor for group relations. This question is of renewed interest today, when theorists of multiculturalism turn toward love in their search for new models of particularity and universality. The book is structured around two transformative moments in German Jewish culture

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Markwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.

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Why do states frequently reject coercive threats from more powerful opponents? This introductory chapter begins by outlining the explanations in the existing literature for failures of coercive diplomacy. It suggests that these accounts generally share a cognitivist perspective that neglects the role of emotion in target leaders’ decision-making. To capture the social, physiological, and dynamic nature of emotion, it is necessary to introduce an additional action model besides the traditional rationalist and constructivist paradigms. The chapter provides a summary of this logic of affect, or e

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Pick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.

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The ‘Introduction’ provides an overview of psychoanalysis, its history, and its development. Psychoanalysis is an original method of therapy that is a form of inquiry, a theory of mind, and a mode of treatment concerned, above all, with the unconscious mind. Founded by Sigmund Freud (1856–1939), it became a movement and set of institutions, inspiring many, but also galvanizing numerous opponents. Freud’s method of free association involves allowing the patient to discuss anything that comes into their mind. The analyst is tasked with attending to possible unconscious meaning in what the patien

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Book chapters on the topic "Opponent model"

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Donkers, Jeroen, Jaap van den Herik, and Jos Uiterwijk. "Probabilistic Opponent-Model Search in Bao." In Entertainment Computing – ICEC 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28643-1_53.

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Chang, Hung-Jui, Cheng Yueh, Gang-Yu Fan, Ting-Yu Lin, and Tsan-sheng Hsu. "Opponent Model Selection Using Deep Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11488-5_16.

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van der Zwet, Koen, Ana Isabel Barros, Tom M. van Engers, and Bob van der Vecht. "An Agent-Based Model for Emergent Opponent Behavior." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22741-8_21.

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van Galen Last, Niels. "Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation." In New Trends in Agent-Based Complex Automated Negotiations. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24696-8_12.

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Donkers, H. H. L. M., H. J. Herik, and J. W. H. M. Uiterwijk. "Opponent-Model Search in Bao: Conditions for a Successful Application." In Advances in Computer Games. Springer US, 2004. http://dx.doi.org/10.1007/978-0-387-35706-5_20.

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Salam, Khan Md Mahbubush, and Kazuyuki Ikko Takahashi. "Mathematical model of conflict and cooperation with non-annihilating multi-opponent." In Unifying Themes in Complex Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-85081-6_38.

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Black, Elizabeth, and Anthony Hunter. "Reasons and Options for Updating an Opponent Model in Persuasion Dialogues." In Theory and Applications of Formal Argumentation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28460-6_2.

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Bullock, Daniel, José L. Contreras-Vidal, and Stephen Grossberg. "Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control." In Neural Networks in Robotics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_25.

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Zhang, Yicheng, Jiannan Zhao, Mu Hua, et al. "O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_21.

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Mudgal, Chhaya, and Julita Vassileva. "Bilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent." In Cooperative Information Agents IV - The Future of Information Agents in Cyberspace. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-45012-2_11.

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Conference papers on the topic "Opponent model"

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Zhang, Jiajing, Jiamei Jiang, Linjing Li, and Daniel Zeng. "A Novel Decision-Making Model for Playing Board Game Combining Planning and Opponent Behaviors." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889240.

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Yu, XiaoPeng, Wanpeng Zhang, and Zongqing Lu. "LLM-Based Explicit Models of Opponents for Multi-Agent Games." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.41.

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Nakano, Yasuhisa. "New model for brightness perception." In Advances in Color Vision. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fd5.

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Brightness-luminance discrepancy is the well known issue in color vision. To overcome this discrepancy, several models of brightness perception are proposed1),2) based on color vision model. Common idea of these models is that the brightness-luminance discrepancy yield from contributions of red-green and yellow-blue color opponent channels to the brightness perception. Recently, however, Nakano, Ikeda and Kaiser (1988)3) proposed another type of model. They explained the brightness perception using L-M and M-L type opponent mechanisms in stead of luminance and color opponent channels. They als

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Zhang, Jiajing, Jiamei Jiang, Linjing Li, and Daniel Zeng. "BG-Planner: A Planning-Based Decision-Making Model for Playing Board Game." In 31st International Conference on Neural Information Processing. Tuwhera, 2025. https://doi.org/10.24135/iconip22.

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Board game offers a unique platform for exploring the capabilities of artificial intelligence in decision-making. It demands long-term strategic planning and opponent behaviors to refine decision-making. Since the success of AlphaGo family, learning agents have become pivotal methods for board game. However, current learning agents rarely incorporate planning or build interactive loops with opponents' behaviors in decision-making. This paper proposes a novel planning-based model (BG-Planner) for strategic decision-making and long-term planning in board game. We propose a Graphplan-style networ

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Hernandez, Daniel, Hendrik Baier, and Michael Kaisers. "BRExIt: On Opponent Modelling in Expert Iteration." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/422.

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Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporating opponent models into the state-of-the-art learning algorithm Expert Iteration (ExIt). BRExIt aims to (1) improve feature shaping in the apprentice, with a policy head predicting opponent policies as

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Ahumada, Albert J. "Learning a red–green opponent system from LGN inputs." In OSA Annual Meeting. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.wr5.

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Ahumada and Mulligan1 proposed a network model for constructing a red–green opponent system from LGN outputs without specific long versus middle wavelength cone labeling. They constructed model LGN cells having long and middle cone inputs and fed the LGN outputs into units, presumed cortical, which were trained by a network learning process to compute the principal component of the LGN units in its receptive field. These units turn out approximately doubly opponent and have less luminance sensitivity than their LGN input cells. Their outputs were then calibrated by translation invariance. Derr

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Gershon, Ron, and John K. Tsotsos. "Experiments with a spatiochromatic model of early vision." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wd2.

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We claim that color vision provides important information about properties of surfaces. To process the chromatic information in images, one has to use a model of color vision and determine its responses to different types of input. Our model consists of different operators which resemble in their characteristics the different layers in the biological chromatic visual pathways. In particular, we look at color-opponent and double-opponent cells with center-surround spatial organization. We characterize the mathematical properties of these units and test their responses with respect to changes in

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Zhang, Weinan, Xihuai Wang, Jian Shen, and Ming Zhou. "Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/466.

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This paper investigates the model-based methods in multi-agent reinforcement learning (MARL). We specify the dynamics sample complexity and the opponent sample complexity in MARL, and conduct a theoretic analysis of return discrepancy upper bound. To reduce the upper bound with the intention of low sample complexity during the whole learning process, we propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy Optimization (AORPO). In AORPO, each agent builds its multi-agent environment model, consisting of a dynamics model and multiple opponent models,

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Howett, Gerald L. "Linear opponent-colors model optimized for brightness prediction." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wu3.

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Formal multivariate optimization techniques were applied in an attempt to determine how well a linear, opponent-colors model of color vision could account for specific brightness-matching data. The data fitted were from the Sanders-Wyszecki experiment that matched an adjustable white light in brightness to each of a set of lights of ninety-six different chromaticities and constant luminance. A generalized, linear, opponent-colors model was formulated, which includes the linear models of Guth (and co-workers), Ingling (and co-workers), and Thornton as special cases. The model contained ten para

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Tian, Zheng, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou, and Jun Wang. "A Regularized Opponent Model with Maximum Entropy Objective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/85.

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In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality". In this paper, we redefine the binary random variable o in multi-agent setting and formalize multi-agent reinforcement learning (MARL) as probabilistic inference. We derive a variational lower bound of the likelihood of achieving the optimality and name it as Regularized Opponent Model with Maximum Entropy Objective (ROMMEO). From ROMMEO, we present a novel perspective on opponent modeling and show how it can improve th

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Reports on the topic "Opponent model"

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Howett, Gerald L. Linear opponent-colors model optimized for brightness prediction. National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.85-3202.

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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdra

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Millán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, 1999. http://dx.doi.org/10.18235/0006812.

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Almost two decades after the beginning of the Chilean and English experiments in power sector reform and privatization, many other countries have adopted or are in the process of adopting a model that promotes competition in the wholesale power market that is based partly on the pioneering efforts of those two countries. Some countries which adopted the English model but whose systems are dominated by hydroelectric power found themselves constrained by a structure that did not apply to their particular situations. And now, England and Chile are themselves radically revising their power trading

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Sharman, Jason. The Sixth Bahamas Conference on Financial Crime. Edited by Francesco De Simone and Guillermo Lagarda. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013501.

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The Sixth Bahamas Conference on Financial Crime, co-hosted by the Central Bank of The Bahamas and the Inter-American Development Bank in January 2025, brought together over 390 participants, both in-person and online, including policymakers, regulators, academics, private sector professionals, and representatives from regional and international institutions. The conference featured research papers, grouped into five thematic blocks: the overall effectiveness of anti-money laundering (AML) frameworks; the role of big data and new technologies; applied insights from regulators and compliance pro

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