An agent-based debiasing framework for investment decision-support systems

Bhandari, Gokul; Hassanein, Khaled

doi:10.1080/0144929x.2010.499477

Cited by 21 publications

(15 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Identifying biases also provides an opportunity to enhance decision making in practice. For example, decision support systems based on models of optimal decision making have been implemented to help investors overcome biases to improve returns (e.g., Bhandari & Hassanein, 2012 ).…”

mentioning

confidence: 99%

Departures from optimality when pursuing multiple approach or avoidance goals.

Ballard¹,

Yeo²,

Neal³

et al. 2016

Journal of Applied Psychology

View full text Add to dashboard Cite

This article examines how people depart from optimality during multiple-goal pursuit. The authors operationalized optimality using dynamic programming, which is a mathematical model used to calculate expected value in multistage decisions. Drawing on prospect theory, they predicted that people are risk-averse when pursuing approach goals and are therefore more likely to prioritize the goal in the best position than the dynamic programming model suggests is optimal. The authors predicted that people are risk-seeking when pursuing avoidance goals and are therefore more likely to prioritize the goal in the worst position than is optimal. These predictions were supported by results from an experimental paradigm in which participants made a series of prioritization decisions while pursuing either 2 approach or 2 avoidance goals. This research demonstrates the usefulness of using decision-making theories and normative models to understand multiple-goal pursuit.

show abstract

mentioning

confidence: 99%

Departures from optimality when pursuing multiple approach or avoidance goals.

Ballard¹,

Yeo²,

Neal³

et al. 2016

Journal of Applied Psychology

View full text Add to dashboard Cite

show abstract

“…Over the past several decades, research efforts have been made to study cognitive biases and how they can influence decision making [41,44,66,110,111]. A deeper understanding of cognitive biases can assist in effective debiasing and re-biasing measures for better decision making [112][113][114]. Cognitive biases have been studied extensively in the sociological and psychological fields, yet these intellectual outputs have rarely been considered in other research domains [112], such as in the stormwater management sector.…”

Section: Interpretations Through Cognitive Biasesmentioning

confidence: 99%

“…ABM is better at capturing the non-linear interactions between human behaviors based on various factors and the macro-environment through feedback effects and at explaining the collective outcomes resulted from a given set of interactions among individuals. So far, the primary use of ABM for policy decision support has primarily been in the fields of sociology, epidemiology, and urban planning [75,114,[141][142][143].…”

Section: Applied Agent-based Modeling In Quantitative Decision Supportmentioning

confidence: 99%

Social Barriers and the Hiatus from Successful Green Stormwater Infrastructure Implementation across the US

Barclay

2021

Hydrology

View full text Add to dashboard Cite

Green stormwater infrastructure (GSI), a nature-inspired, engineered stormwater management approach, has been increasingly implemented and studied especially over the last two decades. Though recent studies have elucidated the social benefits of GSI implementation in addition to its environmental and economic benefits, the social factors that influence its implementation remain under-explored thus, there remains a need to understand social barriers on decisions for GSI. This review draws interdisciplinary research attention to the connections between such social barriers and the potentially underlying cognitive biases that can influence rational decision making. Subsequently, this study reviewed the agent-based modeling (ABM) approach in decision support for promoting innovative strategies in water management for long-term resilience at an individual level. It is suggested that a collaborative and simultaneous effort in governance transitioning, public engagement, and adequate considerations of demographic constraints are crucial to successful GSI acceptance and implementation in the US.

show abstract

“…One approach how researcher classified cognitive biases is exploratory, focusing on the recognition of cognitive biases in decision-making processes. On this basis, cognitive biases were clustered using different procedures such as card sorting [28,29], perceived similarities [5,30,31], and a generic structure of decision-making processes [6,[32][33][34].…”

Section: Analysis Of Existing Classificatory Frameworkmentioning

confidence: 99%

BIASMAP – Developing a Visual Typology and Interface to Explore and Understand Decision-Making Errors in Management

Eppler

Muntwiler

2021

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

In this paper we develop a novel interactive and highly visual interface to make current research about cognitive biases in strategic decision-making processes widely available to non-experts in management, administration, and engineering. Cognitive biases play a major role in the process of strategic decisionmaking. Thus, the recognition, compilation, and understanding of these biases is crucial to develop approaches to debias decisions. The literature on cognitive biases is highly fragmented and includes various research fields. This makes it difficult to gain an overview or find relevant biases quickly. To structure this knowledge, we have developed a typology of cognitive biases based on existing classificatory frameworks and an integrative literature analysis. This led to a two-dimensional typology combining the psychological view on cognitive biases with a decision process-oriented perspective. As a result, knowledge about 187 cognitive biases that affects decision-making is presented in a practice-oriented structure (viewable at bias.visual-literacy.org).

show abstract

An agent-based debiasing framework for investment decision-support systems

Cited by 21 publications

References 47 publications

Departures from optimality when pursuing multiple approach or avoidance goals.

Departures from optimality when pursuing multiple approach or avoidance goals.

Social Barriers and the Hiatus from Successful Green Stormwater Infrastructure Implementation across the US

BIASMAP – Developing a Visual Typology and Interface to Explore and Understand Decision-Making Errors in Management

Contact Info

Product

Resources

About