Behavioral Economics: a Primer and Applications to the UN Sustainable Development Goal of Good Health and Well-Being
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Notes on Behavioral Economics and Labor Market Policy
Notes on Behavioral Economics and Labor Market Policy Linda Babcock, Carnegie Mellon University William J. Congdon, The Brookings Institution Lawrence F. Katz, Harvard University Sendhil Mullainathan, Harvard University December 2010 We are grateful to Jeffrey Kling for extensive discussions of these topics. We thank the Alfred P. Sloan Foundation and the Russell Sage Foundation for their support of this project. 0. Background and motivation Recent years have been trying ones for American workers. The unemployment rate has reached double digits for the first time in over a quarter of a century. Worker compensation growth has all but stalled. The human costs of labor market turbulence have rarely been clearer, and the value of public policies, such as unemployment insurance and job training programs, that assist workers in managing that turbulence, gaining new skills, and navigating the labor market have rarely been more apparent. And, even in the best of times, the United States’ labor market is a dynamic and turbulent one, with high rates of turnover (over five million separations and five million new hires in a typical month in normal times) but substantial frictions as well. As a result, labor market programs and regulations are key components of economic policy. Such policies help support the unemployed, provide education and training opportunities, and ensure the fairness, safety, and accessibility of the workplace. The challenge for policymakers is to design such policies so that they meet these goals as effectively and as efficiently as possible. Labor market policies succeed in meeting their objectives, however, only to the extent that they accurately account for how individuals make decisions about work and leisure, searching for jobs, and taking up opportunities for education and training. -
A Task-Based Taxonomy of Cognitive Biases for Information Visualization
A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias, -
Decision Making and Keynesian Uncertainty in Financial Markets: an Interdisciplinary Approach
Decision making and Keynesian uncertainty in financial markets: an interdisciplinary approach Ms Eirini Petratou, PhD candidate in Economics division, Leeds University Business School Abstract: This interdisciplinary research examines decision-making in financial markets, regarding secondary markets and financial innovation in Europe, so as to unveil the multiple dimensions of this complexly-layered world of financial interactions. Initially, I synthesise different literatures, including Post-Keynesian economics, behavioural economics and finance, and decision-making theory, in order to shape a more complete understanding of behaviours and outcomes in financial markets. Particularly, I focus on an analysis of Keynesian uncertainty in order to show the need of economic science for interdisciplinary research, on theoretical and methodological levels, and also to highlight potential links between Post-Keynesian theory and behavioural economics. After presenting the active role of decision-making under uncertainty into the creation of financial bubbles, as incorporated in Post-Keynesian economics, I support my argument by presenting findings obtained by a series of semi-structured interviews I conducted in 2016 in the UK, in order to investigate financial traders’ beliefs, perspectives and terminology about decision-making under uncertainty in financial markets. The goal of this qualitative project is to identify market professionals’ beliefs about market uncertainty and how they deal with it, to find out their knowledge about causes of the -
1 GEORGE A. AKERLOF Addresses
1 GEORGE A. AKERLOF Addresses: Department of Economics 549 Evans Hall -- #3880 University of California Berkeley, California 94720-3880 (510) 642-5837 E-mail: [email protected] Education: l966 Ph.D., M.I.T. 1962 B.A., Yale University Employment: l980- Professor, University of California at Berkeley 1994- Senior Fellow, The Brookings Institution l978-l980 Cassel Professor with respect to Money and Banking, London School of Economics l977-l978 Visiting Research Economist, Special Studies Section, Board of Governors of the Federal Reserve System l977-l978 Professor, University of California, Berkeley l973-l974 Senior Staff Economist, Council of Economic Advisors l970-l977 Associate Professor, University of California at Berkeley l969-Summer Research Associate, Harvard University l967-l968 Visiting Professor, Indian Statistical Institute 1966-1970 Assistant Professor, University of California at Berkeley Honors and Awards: Senior Adviser, Brookings Panel on Economic Activity Executive Committee of the American Economics Association Vice-President, American Economic Association Associate Editor, American Economic Review, Quarterly Journal of Economics, Journal of Economic Behavior and Organization 2 Co-editor, Economics and Politics Guggenheim Fellowship Fulbright Fellowship Fellow of the Econometric Society North American Council of the Econometric Society Fellow of the American Academy of Arts and Sciences 1990 Ely Lecturer of the American Economic Association Associate, Economic Growth Program, Canadian Institute for Advanced Research -
Bounded Rationality Till Grüne-Yanoff* Royal Institute of Technology, Stockholm, Sweden
Philosophy Compass 2/3 (2007): 534–563, 10.1111/j.1747-9991.2007.00074.x Bounded Rationality Till Grüne-Yanoff* Royal Institute of Technology, Stockholm, Sweden Abstract The notion of bounded rationality has recently gained considerable popularity in the behavioural and social sciences. This article surveys the different usages of the term, in particular the way ‘anomalous’ behavioural phenomena are elicited, how these phenomena are incorporated in model building, and what sort of new theories of behaviour have been developed to account for bounded rationality in choice and in deliberation. It also discusses the normative relevance of bounded rationality, in particular as a justifier of non-standard reasoning and deliberation heuristics. For each of these usages, the overview discusses the central methodological problems. Introduction The behavioural sciences, in particular economics, have for a long time relied on principles of rationality to model human behaviour. Rationality, however, is traditionally construed as a normative concept: it recommends certain actions, or even decrees how one ought to act. It may therefore not surprise that these principles of rationality are not universally obeyed in everyday choices. Observing such rationality-violating choices, increasing numbers of behavioural scientists have concluded that their models and theories stand to gain from tinkering with the underlying rationality principles themselves. This line of research is today commonly known under the name ‘bounded rationality’. In all likelihood, the term ‘bounded rationality’ first appeared in print in Models of Man (Simon 198). It had various terminological precursors, notably Edgeworth’s ‘limited intelligence’ (467) and ‘limited rationality’ (Almond; Simon, ‘Behavioral Model of Rational Choice’).1 Conceptually, Simon (‘Bounded Rationality in Social Science’) even traces the idea of bounded rationality back to Adam Smith. -
Neuroeconomics and the Study of Addiction
Neuroeconomics and the Study of Addiction John Monterosso, Payam Piray, and Shan Luo We review the key findings in the application of neuroeconomics to the study of addiction. Although there are not “bright line” boundaries between neuroeconomics and other areas of behavioral science, neuroeconomics coheres around the topic of the neural representations of “Value” (synonymous with the “decision utility” of behavioral economics). Neuroeconomics parameterizes distinct features of Valuation, going beyond the general construct of “reward sensitivity” widely used in addiction research. We argue that its modeling refinements might facilitate the identification of neural substrates that contribute to addiction. We highlight two areas of neuroeconomics that have been particularly productive. The first is research on neural correlates of delay discounting (reduced Valuation of rewards as a function of their delay). The second is work that models how Value is learned as a function of “prediction-error” signaling. Although both areas are part of the neuroeconomic program, delay discounting research grows directly out of behavioral economics, whereas prediction-error work is grounded in models of learning. We also consider efforts to apply neuroeconomics to the study of self-control and discuss challenges for this area. We argue that neuroeconomic work has the potential to generate breakthrough research in addiction science. Key Words: Addiction, behavioral economics, delay discounting, limbic targets, especially the ventral striatum (3). One hypothesis is neuroeconomics, prediction error, substance dependence that individuals with relatively weak responses to reward “tend to be less satisfied with natural rewards and tend to abuse drugs and he behavior of someone with an addiction can be frustrating alcohol as a way to seek enhanced stimulation of the reward path- and perplexing. -
Uncertainty, Evolution, and Behavioral Economic Theory
UNCERTAINTY, EVOLUTION, AND BEHAVIORAL ECONOMIC THEORY Geoffrey A. Manne, International Center for Law & Economics Todd J. Zywicki, George Mason University School of Law Journal of Law, Economics & Policy, Forthcoming 2014 George Mason University Law and Economics Research Paper Series 14-04 Uncertainty, Evolution, and Behavioral Economic Theory Geoffrey A. Manne Executive Director, International Center for Law & Economics Todd J. Zywicki George Mason University Foundation Professor of Law George Mason University School of Law Abstract: Armen Alchian was one of the great economists of the twentieth century, and his 1950 paper, Uncertainty, Evolution, and Economic Theory, one of the most important contributions to the economic literature. Anticipating modern behavioral economics, Alchian explains that firms most decidedly do not – cannot – actually operate as rational profit maximizers. Nevertheless, economists can make useful predictions even in a world of uncertainty and incomplete information because market environments “adopt” those firms that best fit their environments, permitting them to be modeled as if they behave rationally. This insight has important and under-appreciated implications for the debate today over the usefulness of behavioral economics. Alchian’s explanation of the role of market forces in shaping outcomes poses a serious challenge to behavioralists’ claims. While Alchian’s (and our) conclusions are born out of the same realization that uncertainty pervades economic decision making that preoccupies the behavioralists, his work suggests a very different conclusion: The evolutionary pressures identified by Alchian may have led to seemingly inefficient firms and other institutions that, in actuality, constrain the effects of bias by market participants. In other words, the very “defects” of profitable firms — from conservatism to excessive bureaucracy to agency costs — may actually support their relative efficiency and effectiveness, even if they appear problematic, costly or inefficient. -
Title the Possibility of Behavioral New Institutional Economics Sub Title Author 菊沢, 研宗
Title The possibility of behavioral new institutional economics Sub Title Author 菊沢, 研宗(Kikuzawa, Kenshu) Publisher Society of Business and Commerce, Keio University Publication year 2011 Jtitle Keio business review No.46(2011) ,p.25(1)- 42(18) Abstract Behavioral economics has recently been the subject of considerable research with the consequence that theories in behavioral economics and finance have complementarily developed to comprise a research field known as 'behavioral finance'. Subsequent studies seeking to integrate game theory and behavioral economics come under the 'behavioral game theory' umbrella, while those wanting to integrate contract theory and behavioral economics fall under 'behavioral contract theory'. Given such circumstances, the remaining avenue to explore is the integration of behavioral economics and new institutional economics, the latter consisting of transaction cost economics, agency theory, and the theory of property rights. This paper pursues this remaining possibility and indeed proves that 'behavioral new institutional economics' can be developed and would be a fruitful new field of research. Notes Genre Journal Article URL https://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=AA002 60481-20110000-0025 慶應義塾大学学術情報リポジトリ(KOARA)に掲載されているコンテンツの著作権は、それぞれの著作者、学会または出版社/発行者に帰属し、その 権利は著作権法によって保護されています。引用にあたっては、著作権法を遵守してご利用ください。 The copyrights of content available on the KeiO Associated Repository of Academic resources (KOARA) belong to the respective authors, academic societies, or publishers/issuers, and these rights are protected by the Japanese Copyright Act. When quoting the content, please follow the Japanese copyright act. Powered by TCPDF (www.tcpdf.org) (25)1 KEIO BUSINESS REVIEW Final version received on 30th November 2011 No. 46, 2011 pp.25–42. -
Ilidigital Master Anton 2.Indd
services are developed to be used by humans. Thus, understanding humans understanding Thus, humans. by used be to developed are services obvious than others but certainly not less complex. Most products bioengineering, and as shown in this magazine. Psychology mightbusiness world. beBe it more the comparison to relationships, game elements, or There are many non-business flieds which can betransfered to the COGNTIVE COGNTIVE is key to a succesfully develop a product orservice. is keytoasuccesfullydevelopproduct BIASES by ANTON KOGER The Power of Power The //PsychologistatILI.DIGITAL WE EDIT AND REINFORCE SOME WE DISCARD SPECIFICS TO WE REDUCE EVENTS AND LISTS WE STORE MEMORY DIFFERENTLY BASED WE NOTICE THINGS ALREADY PRIMED BIZARRE, FUNNY, OR VISUALLY WE NOTICE WHEN WE ARE DRAWN TO DETAILS THAT WE NOTICE FLAWS IN OTHERS WE FAVOR SIMPLE-LOOKING OPTIONS MEMORIES AFTER THE FACT FORM GENERALITIES TO THEIR KEY ELEMENTS ON HOW THEY WERE EXPERIENCED IN MEMORY OR REPEATED OFTEN STRIKING THINGS STICK OUT MORE SOMETHING HAS CHANGED CONFIRM OUR OWN EXISTING BELIEFS MORE EASILY THAN IN OURSELVES AND COMPLETE INFORMATION way we see situations but also the way we situationsbutalsotheway wesee way the biasesnotonlychange Furthermore, overload. cognitive avoid attention, ore situations, guide help todesign massively can This in. take people information of kind explainhowandwhat ofperception egory First,biasesinthecat andappraisal. ory, self,mem perception, into fourcategories: roughly bedivided Cognitive biasescan within thesesituations. forusers interaction andeasy in anatural situationswhichresults sible toimprove itpos and adaptingtothesebiasesmakes ingiven situations.Reacting ways certain act sively helpstounderstandwhypeople mas into consideration biases ing cognitive Tak humanbehavior. topredict likely less or andmore relevant illusionsare cognitive In each situation different every havior day. -
Cognitive Biases in Software Engineering: a Systematic Mapping Study
Cognitive Biases in Software Engineering: A Systematic Mapping Study Rahul Mohanani, Iflaah Salman, Burak Turhan, Member, IEEE, Pilar Rodriguez and Paul Ralph Abstract—One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work. Index Terms—Antecedents of cognitive bias. cognitive bias. debiasing, effects of cognitive bias. software engineering, systematic mapping. 1 INTRODUCTION OGNITIVE biases are systematic deviations from op- knowledge. No analogous review of SE research exists. The timal reasoning [1], [2]. In other words, they are re- purpose of this study is therefore as follows: curring errors in thinking, or patterns of bad judgment Purpose: to review, summarize and synthesize the current observable in different people and contexts. A well-known state of software engineering research involving cognitive example is confirmation bias—the tendency to pay more at- biases. -
Infographic I.10
The Digital Health Revolution: Leaving No One Behind The global AI in healthcare market is growing fast, with an expected increase from $4.9 billion in 2020 to $45.2 billion by 2026. There are new solutions introduced every day that address all areas: from clinical care and diagnosis, to remote patient monitoring to EHR support, and beyond. But, AI is still relatively new to the industry, and it can be difficult to determine which solutions can actually make a difference in care delivery and business operations. 59 Jan 2021 % of Americans believe returning Jan-June 2019 to pre-coronavirus life poses a risk to health and well being. 11 41 % % ...expect it will take at least 6 The pandemic has greatly increased the 65 months before things get number of US adults reporting depression % back to normal (updated April and/or anxiety.5 2021).4 Up to of consumers now interested in telehealth going forward. $250B 76 57% of providers view telehealth more of current US healthcare spend % favorably than they did before COVID-19.7 could potentially be virtualized.6 The dramatic increase in of Medicare primary care visits the conducted through 90% $3.5T telehealth has shown longevity, with rates in annual U.S. health expenditures are for people with chronic and mental health conditions. since April 2020 0.1 43.5 leveling off % % Most of these can be prevented by simple around 30%.8 lifestyle changes and regular health screenings9 Feb. 2020 Apr. 2020 OCCAM’S RAZOR • CONJUNCTION FALLACY • DELMORE EFFECT • LAW OF TRIVIALITY • COGNITIVE FLUENCY • BELIEF BIAS • INFORMATION BIAS Digital health ecosystems are transforming• AMBIGUITY BIAS • STATUS medicineQUO BIAS • SOCIAL COMPARISONfrom BIASa rea• DECOYctive EFFECT • REACTANCEdiscipline, • REVERSE PSYCHOLOGY • SYSTEM JUSTIFICATION • BACKFIRE EFFECT • ENDOWMENT EFFECT • PROCESSING DIFFICULTY EFFECT • PSEUDOCERTAINTY EFFECT • DISPOSITION becoming precise, preventive,EFFECT • ZERO-RISK personalized, BIAS • UNIT BIAS • IKEA EFFECT and • LOSS AVERSION participatory. -
A Heuristic Study of the Process of Becoming an Integrative Psychotherapist
'This Time it's Personal': A Heuristic Study of the Process of Becoming an Integrative Psychotherapist. A thesis submitted to the University of Manchester for the degree of Professional Doctorate in Counselling Psychology in the Faculty of Humanities 2015 Cathal O’Connor 1 2 Contents Abstract 7 Declaration 8 Copyright Statement 9 Dedication and Acknowledgements 11 Chapter 1: Introduction 12 Introduction 12 The person of the researcher: What's past is 12 prologue There is an 'I' in thesis 13 Statement of the problem 15 The structure of my research 16 What is counselling psychology? 17 Integration 18 What is psychotherapy? 20 Conclusion 21 Chapter 2: Literature Review 22 Introduction 22 Researching psychotherapy and integration 22 Personal and professional development in 24 counselling psychology training What is personal development? 25 Therapist training 27 Stages of development 34 Supervision 35 Personal development and personal therapy 37 Training in integrative psychotherapy 41 Developing an integrative theory of practice 46 Journaling for personal and professional 48 development Conclusion 57 Chapter 3: Methodology 59 Introduction 59 3 Philosophical positioning 59 Psychology and counselling psychology 60 Epistemology and the philosophy of science 60 The philosophy of scientific psychology 62 Qualitative research 63 Phenomenology 64 Research design 65 Initial engagement 69 The research question 71 Immersion 73 Incubation 76 Illumination 78 Explication 79 Creative synthesis 80 Data generation: Journaling 80 Data analysis: Thematic analysis