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Готові списки джерел за темами / Bounded and Ecological Rationality

Добірка наукової літератури з теми "Bounded and Ecological Rationality"

Автор: Grafiati

Опубліковано: 7 липня 2024

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Статті в журналах з теми "Bounded and Ecological Rationality":

1

Berg, Nathan. "The consistency and ecological rationality approaches to normative bounded rationality." Journal of Economic Methodology 21, no. 4 (October 2, 2014): 375–95. http://dx.doi.org/10.1080/1350178x.2014.969910.

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2

Lejarraga, José, and Maud Pindard-Lejarraga. "Bounded Rationality: Cognitive Limitations or Adaptation to the Environment? The Implications of Ecological Rationality for Management Learning." Academy of Management Learning & Education 19, no. 3 (September 2020): 289–306. http://dx.doi.org/10.5465/amle.2019.0189.

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3

Van der Merwe, Ragnar. "Rational Decision-Making in a Complex World." Logos & Episteme 13, no. 4 (2022): 381–404. http://dx.doi.org/10.5840/logos-episteme202213432.

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Prima facie, we make successful decisions as we act on and intervene in the world day-to-day. Epistemologists are often concerned with whether rationality is involved in such decision-making practices, and, if so, to what degree. Some, particularly in the post-structuralist tradition, argue that successful decision-making occurs via an existential leap into the unknown rather than via any determinant or criterion such as rationality. I call this view radical voluntarism (RV). Proponents of RV include those who subscribe to a view they call Critical Complexity (CC). In this paper, I argue that CC presents a false dichotomy when it conceives of rationality in Cartesian – i.e. ideal and transcendental – terms, and then concludes that RV is the proper alternative. I then outline a pragmatist rationality informed by recent work in psychology on bounded rationality, ecological rationality, and specifically embodied rationality. Such a pragmatist rationality seems to be compatible with the tenets of post-structuralism, and can therefore replace RV in CC.

4

Foxon, Tim. "Bounded rationality and hierarchical complexity: Two paths from Simon to ecological and evolutionary economics." Ecological Complexity 3, no. 4 (December 2006): 361–68. http://dx.doi.org/10.1016/j.ecocom.2007.02.010.

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5

Riva, Silvia, Paola Iannello, and Alessandro Antonietti. "Health comes first: Smart heuristics to stay healthy." RICERCHE DI PSICOLOGIA, no. 3 (December 2021): 1–14. http://dx.doi.org/10.3280/rip2021oa12717.

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In the classical "jacket-calculator" dilemma it is postulated decision is regulated by a mental topical accounting process which orients people to consider a discount price when purchasing items. We proposed an adapted version of the classical "jacket-calculator" task re-framing the choice in a medical context. Our results supported the view that simple minimal mental accounts influence evaluation and choice in the medical context where time, instead of price, represents a fundamental cue of the decision analysis. The decision process adopted by participants tended to be associated with a lexicographic decision mechanism where "time" appears the most effective cue of a "take-the-best" heuristic to predict people's behaviour accurately. These findings broaden the body of evidence indicating that bounded rationality in human decisions is intrinsically connected with the decisional context and different contexts may elicit different mental accounting strategies. In addition, the study stressed the need to enhance the dialogue between the more recent paradigm of the ecological rationality with the classical interpretations of bounded rationality because the two paradigms, not rarely opposed to each other, can provide hints to the interpretation of the decision process, with practical considerations for future interventions in health education and public health.

6

Stajkic, Bojana, and Kaja Damnjanovic. "The road to Nobel prize is paved with the conceptualisations of rationality from homo economicus to homo heuristicus." Theoria, Beograd 61, no. 2 (2018): 147–62. http://dx.doi.org/10.2298/theo1802147s.

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In this paper we present the main psychological conceptions of rationality: unbounded rationality, bounded rationality, optimization under constraints, and ecological rationality. We show how these concepts directed the research questions, and how they shaped psychological models of complex cognitive processes. In its symbolic tradition, for more than a century, the psychology, as a fundamental cognitive science, has been focused on the question of how the environment is represented in the cognitive system, how the cognitive system operates with those information, and, ultimately, what are the outcomes of these processes. The basis on which the research efforts focusing on complex cognitive processes, such as judgment, decision-making, and reasoning - are rooted in is the stance of authors, and psychological models regarding rationality. The conceptualizations of rationality are, at the beginning of the psychological research, implicit, because they are taken from a normative approach, and the research focus is on the outcome of cognitive processes, while the functions and the processes themselves are neglected. Later, as the research diverge from the normative approach, the psychological conceptualization of rationality becomes more explicit and subjective, and more nested in the environment, and the empirical studies aim to describe the structure and dynamics of complex cognitive processes.

7

Prußeit, Tabitha. "Intuiciones y racionalidad ecológica." Revista de Humanidades de Valparaíso, no. 24 (February 29, 2024): 145–62. http://dx.doi.org/10.22370/rhv2024iss24pp145-162.

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Through an ecological approach, it can be said that a person tries to identify the optimal action for him- or herself by a set of different possible actions through intuition. The debate about the ‘dual process theories’ takes up this idea and postulates two different ways to evaluate decision possibilities: A logical-analytical path and an intuitive (Magrabi and Bach, 2013). Predominantly, it is assumed that the logical-analytical path is related to rationality and therefore the path to strive for in rational decision-making. However, in science it has been shown that bounded rationality is to be assumed. Therefore, the question of the extent to which emotions or feelings, and in this context also intuitions, are rational has come to the fore (De Sousa, 1987; Evans and Cruse, 2004). In this paper, intuitions are considered as a feeling with intentionality: Intuitions always refer to an appropriate environment and are directed toward a decision that results in an action. With Damásio (1994, 2013) it can be shown that an intuition is a feeling that excels in acting as a cue or signal. With the assumption of an ecological rationality (Gigerenzer, Todd and ABC Research Group, 1999; Todd and Gigerenzer, 2012; Todd and Brighton, 2016), it can be shown that intuitive decisions are based on experiential knowledge and implicit structure recognition (Magrabi and Bach, 2013). Accordingly, with the approach of an ecological rationality it can be shown that an intuitive decision is rational in the sense that a person interacts quickly and energy-efficiently successfully adapted with his or her environment and thereby identifies the individual optimal action for him- or herself. This may then be recognized as a ‘gut feeling’ or ‘intuition’. Two challenges will be outlined explained in this paper: The influence of a negative environment on intuitive decisions, and the confusion of an intuition with an emotion or other feeling.

8

Farias, Hilder André Bezerra, Sérgio Luiz de Medeiros Rivero, and Márcia Jucá Teixeira Diniz. "Negative incentives and sustainability in the amazonian logging industry." Nova Economia 27, no. 3 (December 2017): 363–91. http://dx.doi.org/10.1590/0103-6351/2735.

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Abstract: This paper investigates the existence of lock-in of low technology and high environmental impact on Brazilian Amazon logging industry. The research employed evolutionary economics as a theoretical basis, especially the concept of technological trajectories. The duality of decisions involving logging - conventional logging (CL) versus reduced-impact logging (RIL) - was studied. An agent-based simulation model - in which decision-making under bounded rationality is based on a genetic algorithm - was implemented in Java programming language. Results demonstrate the existence of lock-in, producers aversion to risks, greater operational efficiency of sustainable logging, and benefits derived from a policy of environmental bonuses, both in economic and ecological terms.

9

Choudhury, Avishek. "Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians." JMIR Human Factors 9, no. 2 (June 21, 2022): e35421. http://dx.doi.org/10.2196/35421.

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The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI’s performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI’s ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians’ intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.

10

Mainzer, Klaus. "Challenges of Complexity in the 21st Century. An Interdisciplinary Introduction." European Review 17, no. 2 (May 2009): 219–36. http://dx.doi.org/10.1017/s1062798709000714.

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The theory of nonlinear complex systems has become a proven problem-solving approach in the natural sciences from cosmic and quantum systems to cellular organisms and the brain. Even in modern engineering science self-organizing systems are developed to manage complex networks and processes. It is now recognized that many of our ecological, social, economic, and political problems are also of a global, complex, and nonlinear nature. Modern evolutionary economics can be modelled in the framework of complex systems and nonlinear dynamics. Historically, evolutionary economics was inspired by Schumpeterian concepts of business cycles and innovation dynamics. What are the laws of sociodynamics? What can we learn from nonlinear dynamics for complexity management in social, economic, financial and political systems? Is self-organization an acceptable strategy to handle the complexity in firms, institutions and organizations? The world-wide crisis of financial markets and economies is a challenge for complexity research. Misleading concepts of linear thinking and mild randomness (e.g. Gaussian distributions of Brownian motion) must be overcome by new approaches of nonlinear mathematics (e.g. non-Gaussian distribution), modelling the wild randomness of turbulence at the stock markets. Systemic crises need systemic answers. Nevertheless, human cognitive capabilities are often overwhelmed by the complexity of nonlinear systems they are forced to manage. Traditional mathematical decision theory assumed perfect rationality of economic agents (homo oeconomicus). Herbert Simon, Nobel Prize laureate of economics and one of the leading pioneers of systems science and cognitive science, introduced the principle of bounded rationality. Therefore, we need new insights into the factual microeconomic behaviour of economic agents by methods of humanities, cognitive and social sciences, which are sometimes called ‘experimental economics’. Social and economic dynamics are interdisciplinary challenges of modern complexity research.

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Дисертації з теми "Bounded and Ecological Rationality":

1

Persson, Magnus. "Bounded Rationality and Exemplar Models." Doctoral thesis, Uppsala University, Department of Psychology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3572.

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Bounded rationality is the study of how human cognition with limited capacity is adapted to handle the complex information structures in the environment. This thesis argues that in order to understand the bounded rationality of decision processes, it is necessary to develop decision theories that are computational process models based upon basic cognitive and perceptual mechanisms. The main goal of this thesis is to show that models of perceptual categorization based on the storage of exemplars and retrieval of similar exemplars whenever a new object is encountered (D. L. Medin & M. M. Schaffer, 1978), can be an important contribution to theories of decision making. Study I proposed, PROBEX (PROBabilities from Exemplars), a model for inferences from generic knowledge. It is a “lazy” algorithm that presumes no pre-computed abstractions. In a computer simulation it was found to be a powerful decision strategy, and it was possible to fit the model to human data in a psychologically plausible way. Study II was a theoretical investigation that found that PROBEX was very robust in conditions where the decision maker has very little information, and that it worked well even under the worst circumstances. Study III empirically tested if humans can learn to use exemplar based or one reason decision making strategies (G. Gigerenzer, P. Todd, & the ABC Research Group, 1999) where it is appropriate in a two-alternative choice task. Experiment 1 used cue structure and presentation format as independent variables, and participants easily used one reason strategies if the decision task presented the information as normal text. The participants were only able to use exemplars if they were presented as short strings of letters. Experiment 2 failed to accelerate learning of exemplar use during the decision phase, by prior exposure to exemplars in a similar task. In conclusion, this thesis supports that there are at least two modes of decision making, which are boundedly rational if they are used in the appropriate context. Exemplar strategies may, contrary to study II, only be used late in learning, and the conditions for learning need to be investigated further.

2

Feufel, Markus Alexander. "Bounded Rationality in the Emergency Department." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1249241698.

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3

Kim, Dong-Gook. "An Analysis of Ecological and Social Rationality: When are Lexicographic Heuristics Preferred?" unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-08182008-212550/.

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Thesis (Ph. D.)--Georgia State University, 2008.
Title from file title page. Thomas Whalen, committee chair; Julian Diaz III, C. S. Thachenkary, Rodney Schultz, committee members. Electronic text (168 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed November 3, 2008. Includes bibliographical references (p. 164-168).

4

Fleischhut, Nadine. "Moral judgment and decision making under uncertainty." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2013. http://dx.doi.org/10.18452/16820.

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Die vorliegende Dissertation untersucht, wie Menschen Urteile und Entscheidungen in moralischen Situationen unter Unsicherheit treffen. In theoretischer Hinsicht wird Verhalten in moralischen Situationen aus der Perspektive begrenzter und ökologischer Rationalität analysiert, die das Zusammenspiel zwischen Kognition und der Struktur der Umwelt betont. Empirisch ist das Ziel, moralische Urteile und Verhalten unter epistemischen Bedingungen zu untersuchen, denen Menschen in der realen Welt begegnen. Das erste Projekt diskutiert aus der Perspektive ökologischer Rationalität wie das Zusammenspiel von Heuristiken und Umwelt hilft, moralisches Verhalten zu verstehen, das inkonsistent erscheint, solange es durch Charaktereigenschaften erklärt wird. Aus dieser Perspektive ist es entscheidend, soziale Umwelten zu untersuchen, da Urteile und Verhalten in moralischen Situationen oft nicht durch speziell moralische Regeln sondern durch moralisch neutrale, soziale Heuristiken entstehen können, die den Zusammenhalt sozialer Gruppen fördern. Das zweite Projekt untersucht empirisch Entscheidungen in sozialen Dilemmata. Die Kernfrage ist, wie Kooperation durch den Risikograd geprägt wird, sowie durch die Art, wie Risikoinformationen erworben werden (durch Beschreibung oder Erfahrung), im Vergleich zu nicht-sozialen Situationen mit gleichem Risiko. Kooperation variierte systematisch mit dem Risikograd, aber wie Informationen erworben wurden war nur in den nicht-sozialen Situationen relevant. Prozessdaten und Teilnehmerberichte legen nahe, dass diese Diskrepanz auf Entscheidungsprozessen beruhen könnte, in denen Erwartungen über das Verhaltens anderer sowie die Höhe der Verdienste wichtiger sind als deren Wahrscheinlichkeit. Das dritte Projekt vergleicht Urteile in moralischen Dilemmata, in denen der Verlauf der Ereignisse noch ungewiss ist (Vorschau), mit Situationen, in denen bereits sicher ist, ob negative Nebeneffekte auftreten oder nicht (Rückschau). Die Ergebnisse zeigen einen Rückschaufehler in moralischen Urteilen sowie bei der Wahrscheinlichkeitseinschätzung der Nebeneffekte. Der Unterschied zwischen moralischen Urteilen unter Sicherheit und Unsicherheit wirft die Frage auf, inwieweit sich empirische Ergebnisse generalisieren lassen, die auf den üblicherweise untersuchten moralischen Dilemmata basieren, wie z.B. den „Trolley“-Fällen, in denen alles mit Sicherheit feststeht.
In this dissertation I investigate how people make judgments and decisions in moral situations under uncertainty. Theoretically, behavior in moral situations is analyzed from the perspective of bounded and ecological rationality, which emphasizes the interplay between cognition and the structure of the environment. Empirically, the goal is to investigate moral judgment and behavior under epistemic conditions people encounter in the real world. The first project discusses from the perspective of ecological rationality how the interaction of heuristics and the environment helps explaining moral behavior that appears inconsistent from accounts referring to characteristics of the individual. This view also emphasizes the importance of studying social environments, as judgment and behavior in moral situations under uncertainty may often not result from specifically moral rules but instead from morally neutral social heuristics which serve the coherence of social groups. The second project empirically examines decisions in social dilemmas. The key question was how cooperation is shaped by different levels of risk and by the way information about risk is acquired (from description or from experience), compared to nonsocial situations with equivalent risks. Cooperation systematically varied with different levels of risk, yet the way in which information was acquired only mattered in nonsocial situations. Process data and self-reports indicated that this discrepancy may have resulted from decision processes that are more sensitive to expectations about others’ behavior and the size of rewards than to reward probabilities. The third project compared judgments in moral dilemmas when the course of events is still uncertain (foresight) with situations when it was already certain whether negative side-effects did or did not occur (hindsight). Results showed a hindsight effect for moral judgments, as well as for probability estimates of negative side-effects. As moral judgments differed under certainty and uncertainty, this raises concerns about generalizing empirical results from commonly investigated moral dilemmas, such as the “trolley” cases, in which everything is certain.

5

Kaur, Surinder. "Culturally bounded rationality." Thesis, Henley Business School, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294508.

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6

Östling, Robert. "Bounded rationality and endogenous preferences." Doctoral thesis, Handelshögskolan i Stockholm, Samhällsekonomi (S), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-454.

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7

Vinokur, Leon. "Environmental policy and bounded rationality." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1708.

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The Prospect Theory proposes to assess outcomes relative to a reference point (or benchmark). Although the literature recognises the relevance of dynamic benchmarks, most of the applications of Prospect Theory employ static reference points (or a status quo). This paper aims to develop a Prospect Theory framework for investment under uncertainty subject to a dynamic reference point, within the context of environmental policy making, where the distinction between a dynamic and a static frameworks is crucial. I evince that, in contrast to the static framework, in a dynamic framework the investor measures not only the absolute but also the relative risk premium (Sharpe ratio) of the investment opportunity, incorporating the risks and returns of a reference portfolio. I propose that there exists a relation between static and dynamic frameworks. Using the dynamic framework, I argue that in the environmental context international co-operation is the key to a successful environmental policy

8

Köhler, Jonathan Hugh. "Bounded rationality in savings decisions." Thesis, University of York, 1996. http://etheses.whiterose.ac.uk/10878/.

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9

Zhang, Luyao. "Bounded Rationality and Mechanism Design." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1532692312980569.

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10

Wu, Yiping. "Bounded rationality for BitTorrent networks." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6681.

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Thesis (M.S.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 19, 2009) Includes bibliographical references.

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Книги з теми "Bounded and Ecological Rationality":

1

Chaudhury, Mahasweta. Bounds of freedom: Popper, liberty and ecological rationality. Amsterdam: Rodopi, 2003.

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2

Salehnejad, Reza. Rationality, bounded rationality and microfoundations. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230625150.

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3

Rubinstein, Ariel. Modeling bounded rationality. Cambridge, Mass: MIT Press, 1998.

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4

Bendor, Jonathan B. Bounded rationality and politics. Berkeley: University of California Press, 2010.

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5

B, Bendor Jonathan, ed. Bounded rationality and politics. Berkeley: University of California Press, 2010.

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6

McCorduck, Pamela. Bounded rationality: A novel. Santa Fe: Sunstone Press, 2012.

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7

Michael, Woodford, and Yannelis Nicholas C, eds. Bounded rationality and learning. Berlin: Springer, 1994.

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8

Munro, Alistair. Bounded Rationality and Public Policy. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1023/b99496.

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9

Onozaki, Tamotsu. Nonlinearity, Bounded Rationality, and Heterogeneity. Tokyo: Springer Japan, 2018. http://dx.doi.org/10.1007/978-4-431-54971-0.

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10

Spiegler, Ran. Bounded rationality and industrial organization. Oxford: Oxford University Press, 2011.

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Частини книг з теми "Bounded and Ecological Rationality":

1

Altman, Morris. "Hayek’s Complexity Assumption, Ecological and Bounded Rationality, and Behavioral Economics." In Hayek and Behavioral Economics, 221–62. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137278159_10.

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2

Marwala, Tshilidzi, and Evan Hurwitz. "Bounded Rationality." In Artificial Intelligence and Economic Theory: Skynet in the Market, 41–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66104-9_4.

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3

Ogaki, Masao, and Saori C. Tanaka. "Bounded Rationality." In Behavioral Economics, 71–82. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6439-5_5.

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4

Secchi, Davide. "Bounded Rationality." In Extendable Rationality, 19–25. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7542-3_3.

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5

Kreps, David M. "Bounded Rationality." In The New Palgrave Dictionary of Economics and the Law, 168–73. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1007/978-1-349-74173-1_37.

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6

Rass, Stefan, Stefan Schauer, Sandra König, and Quanyan Zhu. "Bounded Rationality." In Advanced Sciences and Technologies for Security Applications, 99–114. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46908-5_5.

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7

Tsaoussi, Aspasia. "Bounded Rationality." In Encyclopedia of Law and Economics, 147–51. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7753-2_106.

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8

Tsaoussi, Aspasia. "Bounded Rationality." In Encyclopedia of Law and Economics, 1–6. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7883-6_106-1.

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9

Isler, Ozan. "Bounded Rationality." In Encyclopedia of Evolutionary Psychological Science, 1–3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-16999-6_2156-1.

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Augier, Mie. "Bounded Rationality." In The Palgrave Encyclopedia of Strategic Management, 118–22. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-137-00772-8_533.

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Тези доповідей конференцій з теми "Bounded and Ecological Rationality":

1

Kets, Willemien. "Bounded rationality in games." In the Behavioral and Quantitative Game Theory. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807406.1807434.

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2

Wu, Kan, Fang Liu, Chang-de Lu, and Xi-hui Yang. "Bounded Rationality Modeling in CAID." In 2010 International Conference on Management and Service Science (MASS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmss.2010.5578341.

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3

Braun, Daniel A., Pedro A. Ortega, Evangelos Theodorou, and Stefan Schaal. "Path integral control and bounded rationality." In 2011 Ieee Symposium On Adaptive Dynamic Programming And Reinforcement Learning. IEEE, 2011. http://dx.doi.org/10.1109/adprl.2011.5967366.

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4

Scarsini, Marco, and Tristan Tomala. "Repeated congestion games with bounded rationality." In the Behavioral and Quantitative Game Theory. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807406.1807411.

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5

Ioannou, Christos, and Ioannis Nompel. "Algorithmic bounded rationality, optimality and noise." In the Behavioral and Quantitative Game Theory. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807406.1807489.

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6

Geng, Baocheng, Qunwei Li, and Pramod K. Varshney. "Human Decision Making with Bounded Rationality." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747866.

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Erbas, Cengiz, and Bahar Celikkol Erbas. "Software development under bounded rationality and opportunism." In 2009 ICSE Workshop on Software Development Governance (SDG). IEEE, 2009. http://dx.doi.org/10.1109/sdg.2009.5071331.

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8

Papadimitriou, Christos H., and Mihalis Yannakakis. "On complexity as bounded rationality (extended abstract)." In the twenty-sixth annual ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/195058.195445.

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9

Zhao, Mingyu, Lijun Sun, Nick Tyler, and Cheng Lan. "Embodiment of Bounded Rationality in Travel Choice." In Third International Conference on Transportation Engineering (ICTE). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41184(419)40.

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10

Xu, Tian, Lili Wang, Han-Yue Deng, and Jian-Bei Liu. "Traveling Mode Choice Model under Bounded Rationality." In 19th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2019. http://dx.doi.org/10.1061/9780784482292.055.

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Звіти організацій з теми "Bounded and Ecological Rationality":

1

Farhi, Emmanuel, and Iván Werning. Monetary Policy, Bounded Rationality, and Incomplete Markets. Cambridge, MA: National Bureau of Economic Research, March 2017. http://dx.doi.org/10.3386/w23281.

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2

Gabaix, Xavier. A Sparsity-Based Model of Bounded Rationality. Cambridge, MA: National Bureau of Economic Research, March 2011. http://dx.doi.org/10.3386/w16911.

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3

Angeletos, George-Marios, and Chen Lian. Dampening General Equilibrium: Incomplete Information and Bounded Rationality. Cambridge, MA: National Bureau of Economic Research, February 2022. http://dx.doi.org/10.3386/w29776.

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4

Manski, Charles, and Eytan Sheshinski. Optimal Paternalism in a Population with Bounded Rationality. Cambridge, MA: National Bureau of Economic Research, June 2023. http://dx.doi.org/10.3386/w31349.

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5

Jin, Victoria Y., and Alexander H. Levis. Experimental Design and Evaluation of Bounded Rationality Using Dimensional Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada208209.

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6

Bomfim, Antulio, and Francis Diebold. Bounded Rationality and Strategic Complementarity in a Macroeconomic Model: Policy Effects, Persistence and Multipliers. Cambridge, MA: National Bureau of Economic Research, March 1996. http://dx.doi.org/10.3386/w5482.

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7

Luoto, Jill, Mario Sánchez, Marco Stampini, Jonathan Cali, Diana M. Pinto, Frederico C. Guanais, Pablo Ibarrarán, and Katherine Grace Carman. Applying Behavioral Tools to the Design of Health Projects. Inter-American Development Bank, July 2014. http://dx.doi.org/10.18235/0008508.

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Анотація:

This technical note discusses how behavioral economics insights can be used to enhance the design of health interventions, with emphasis on those addressing non-communicable diseases, by promoting behavioral changes. These interventions are neither sophisticated nor costly, and are particularly suited for cases in which individuals are seemingly not making rational choices about their health. The main contribution of this note is the attempt to orient non-expert practitioners in the analysis of the problem and design of the intervention. For this purpose, we provide a novel framework to map common behavioral biases and barriers that have limited the success of traditional interventions (i.e., bounded rationality, bounded willpower, and bounded selfishness) to the most promising solutions identified in the existing literature (e.g., framing, commitment contracts, and social incentives).