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1 – 10 of over 10000Behavioral decision research focuses on cognitive biases and other barriers to economic rationality. However, if cognitive biases are costly to eliminate, the second-best solution…
Abstract
Behavioral decision research focuses on cognitive biases and other barriers to economic rationality. However, if cognitive biases are costly to eliminate, the second-best solution to bounded rationality may be less rationality rather than more. I define the concept of behavioral rationality and discuss two extreme forms of strategizing, which I call Romantic and Mercenary. Using twentieth century humanitarian Albert Schweitzer as a case study, I discuss the optimization of economic and behavioral rationality. I argue that the success of behavioral strategy as a field does not depend on removing cognitive biases but on helping people deliver more effective strategic actions.
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Venkata Narasimha Chary Mushinada and Venkata Subrahmanya Sarma Veluri
The purpose of this paper is to empirically test the relationship between investors’ rationality and behavioural biases like self-attribution, overconfidence.
Abstract
Purpose
The purpose of this paper is to empirically test the relationship between investors’ rationality and behavioural biases like self-attribution, overconfidence.
Design/methodology/approach
The study applies structural equation modelling to understand whether individual investors, besides being rational, are subjected to self-attribution bias and overconfidence bias.
Findings
The study shows the empirical evidence in the support of behavioural biases like self-attribution and overconfidence existing besides investors’ rationality. Moreover, there is a statistically significant positive covariance found between self-attribution and overconfidence, implying that an increase/decrease in self-attribution results in the increase/decrease in overconfidence and vice versa. It is also observed that the personal characteristics of an investor such as gender, age, occupation, annual income and their trading experience have an impact on behavioural biases.
Research limitations/implications
The study focused on rational decision making, self-attribution and overconfidence biases using primary data. Further studies can be encouraged to test the existence of behavioural biases based on both market level and individual account data simultaneously.
Practical implications
Insights from the study suggest that the investors should perform a post-analysis of each investment, so that they become aware of past behavioural mistakes and stop continuing the same. This might help investors to minimise the negative impact of self-attribution and overconfidence on their expected utility.
Originality/value
To the best of the authors’ knowledge, this is the first study to examine the relationship among investors’ rationality, self-attribution and overconfidence in the Indian context using a comprehensive survey.
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The purpose of this paper is to investigate the relationship between rational decision-making and behavioural biases among individual investors in India, as well as to examine the…
Abstract
Purpose
The purpose of this paper is to investigate the relationship between rational decision-making and behavioural biases among individual investors in India, as well as to examine the influence of demographic variables on rational decision-making process and how those differences manifest themselves in the form of behavioural biases.
Design/methodology/approach
Using a structured questionnaire, a total of 386 valid responses have been collected from May to October 2015. Statistical techniques like t-test, analysis of variance (ANOVA) and Fisher’s least significant difference (LSD) test have been used in this study. Structural equation modelling (SEM) has been used to analyse the relationship between rational decision-making and behavioural biases.
Findings
The findings show that the structural path model closely fits the sample data, indicating investors follow a rational decision-making process while investing. However, behavioural biases also arise in different stages of the decision-making process. It further explores that gender and income have a significant difference with respect to rational decision-making process. Male investors are more prone to overconfidence and herding bias in India.
Research limitations/implications
The findings of the study have significant implication for the individual investors. It is recommended that if individuals are aware about the biases, they may become alert before taking irrational investment decisions.
Originality/value
To best of the authors’ knowledge, the present study is a first of its kind to investigate the relationship between rational decision-making and behavioural biases among individual investors in India.
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Behavioral strategy aspires to build theories that are behaviorally plausible. However, the diversity of human behaviors can make it challenging to know what behavioral…
Abstract
Behavioral strategy aspires to build theories that are behaviorally plausible. However, the diversity of human behaviors can make it challenging to know what behavioral assumptions to use when building theories about organizations and their strategies. Fortunately, organizational contexts are, to varying degrees, designed. This introduces a powerful set of levers – sorting, framing, and structuring – that reduce this diversity of behavioral possibilities to a tractable yet plausible few. Attention to the organizational contexts that shape individual and group behavior can, therefore, help behavioral strategists attain their objectives of building theories with sound behavioral foundations.
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Iris Lorscheid and Matthias Meyer
This study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be…
Abstract
Purpose
This study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be used to systematically derive and formalize different models of BR. This allows us to identify the cognitive preconditions for behavior intended by the mechanism and thereby to derive implications for the design of mechanisms.
Design/methodology/approach
Based on an analysis of the requirements of the decision context, the authors describe a systematic way of incorporating different BR concepts into an agent learning model. The approach is illustrated by analyzing an incentive scheme suggested for truthful reporting in budgeting contexts, which is an adapted Groves mechanism scheme.
Findings
The study describes systematic ways in which to derive BR agents for research questions where behavioral aspects might matter. The authors show that BR concepts may lead to other outcomes than the intended truth-inducing effect. A modification of the mechanism to more distinguishable levels of payments improves the results in terms of the intended effect.
Research limitations/implications
The presented BR concepts as simulated by agent models cannot model human behavior in its full complexity. The simplification of complex human behavior is a useful analytical construct for the controlled analysis of a few aspects and an understanding of the potential consequences of those aspects of human behavior for mechanism design.
Originality/value
The paper specifies the idea of a computational testbed for mechanism design based on BR concepts. Beyond this, a systematic and stepwise approach is shown to formalize bounded rational behavior by agents based on a requirements analysis, including benchmark models for the comparison and evaluation of BR concepts.
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Larry Wofford, Michael Troilo and Andrew Dorchester
This paper seeks to consider selected aspects of the relationship between real estate valuation, human cognition, and translational research. Its purpose is to introduce the…
Abstract
Purpose
This paper seeks to consider selected aspects of the relationship between real estate valuation, human cognition, and translational research. Its purpose is to introduce the concept of cognitive risk, to propose a framework for mitigating it, and to develop a stream of translational research to transfer knowledge to real estate valuers.
Design/methodology/approach
The paper takes an interdisciplinary conceptual approach towards the development and study of cognitive risk, and its mitigation. It proposes to broaden the study of behavioral issues in real estate valuation beyond cognitive psychology to cognitive science, and also fields such as time studies and human failure, in order to identify and mitigate cognitive risk.
Findings
The paper offers a framework as a starting‐point for handling cognitive risk. It borrows the concept of translational research from medicine to discuss how basic theoretical knowledge may be communicated to real estate valuers to improve performance.
Originality/value
The paper's concept of cognitive risk and discussion of its mitigation will enrich behavioral real estate by introducing the wisdom of other fields such as cognitive science and time studies. These fields have much to say about managing the risk surrounding human cognition, and will be of both academic and practical value to the discipline of real estate valuation.
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This paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on…
Abstract
Purpose
This paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.
Design/methodology/approach
An expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.
Findings
The results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.
Research limitations/implications
It is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.
Originality/value
This novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.
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The purpose of this paper is to present a review as well as a synthesis of the extant literature in the field of Neurofinance. The paper has been divided into eight parts. The…
Abstract
Purpose
The purpose of this paper is to present a review as well as a synthesis of the extant literature in the field of Neurofinance. The paper has been divided into eight parts. The first and second parts introduce the paper and dwell upon the brain functions in financial decisions. Part three presents the origin of Neurofinance and part four explains the difference between traditional finance, behavioural finance and neurofinance. Part five and six of the paper look into the research studies in Neurofinance and their application. Part seven gives a brief discussion on the limitations of neurofinance studies and part eight gives the conclusion.
Design/methodology/approach
The existing body of academic literature pertinent to the domain of Neurofinance was reviewed so as to provide an integrated portrayal and synthesis of the current level of knowledge in this field. This paper covers the insights on the subject for developing a deeper understanding of the investor's psychology.
Findings
Neurofinance is a very young discipline. It tries to relate the brain processes to the investment behaviour. Most of the researches in the domain of neurofinance focus on trading behaviour. It would be interesting to explore the workings of the brain for other investment behaviours too like personal financial planning decisions, etc.
Originality/value
Neurofinance is emerging as an alternate field of study and practice and this paper is an attempt to look at the development of Neurofinance and its role in developing a better understanding of the investor behaviour.
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Meisam Mozafar, Alireza Moini and Yaser Sobhanifard
This study aims to identify the origins, mechanisms and outcomes of applying behavioral insight in public policy research.
Abstract
Purpose
This study aims to identify the origins, mechanisms and outcomes of applying behavioral insight in public policy research.
Design/methodology/approach
The authors conducted a systematic literature review to answer three research questions. The authors identified 387 primary studies, dated from January 2000 to April 2021 and coded them through a thematic analysis. Related studies were obtained through searching in Emerald, ScienceDirect, Sage, Springer, Wiley and Routledge.
Findings
The results identified eight themes for origins, 16 themes for mechanisms/techniques and 13 outcome-related themes. Through the thematic analysis, the major mechanisms of behavioral approach were found to be social marketing, information provision, social norms, incentives, affect, regulation design, framing, salience, defaults, simplification, networking, environment design, scheduled announcements, commitments, attitude-preference-behavior manifestation and combining behavioral and nonbehavioral mechanisms.
Practical implications
The findings of this review help policymakers to design or redesign policy elements.
Originality/value
This review provides the first systematic exploration of the existing literature on behavioral public policy.
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