Introduction

When managers decide whether to invest in a new project, their decisions can be influenced by factors such as how information is presented or their mood on that day, despite thorough analysis. These inconsistencies are common and rooted in human psychology. Behavioural Decision Theory (BDT) explores the psychological mechanisms behind human decisions, highlighting frequent inconsistencies and biases. Influenced by researchers like Daniel Kahneman, Amos Tversky, and Paul Slovic, BDT offers crucial insights into the irregularities of decision making behaviour (Kahneman and Tversky, 1974).

BDT reveals that decision making is not always rational but often influenced by cognitive biases and situational factors. For instance, preference reversals – where preferences change based on presentation – challenge the idea of stable preferences. The biases and heuristics programme identifies systematic deviations from rationality due to mental shortcuts, leading to predictable errors (Tversky et al., 1990). Researchers like Gerd Gigerenzer argue that these heuristics can be adaptive in real-world settings, offering a nuanced view of decision making (Gigerenzer, 2015 ; Luan et al., 2019).

Additionally, the concept of noise, as explored by Olivier Sibony, Cass Sunstein, and Daniel Kahneman, introduces random variability in judgments, further complicating efforts to achieve consistent decision outcomes (Sibony et al., 2021). By integrating these perspectives, BDT provides a comprehensive framework for understanding human decision making. This framework is essential for practical applications, such as behavioural change management, where recognising and mitigating cognitive biases and inconsistencies can lead to more effective decision making within organisations.

Key Concepts in Behavioural Decision Theory

Preference Reversal

Preference reversals occur when individuals switch their preference between two options depending on how they are evaluated, even if the options themselves remain constant. Research by Tversky, Slovic, and Kahneman attributes these reversals to different evaluation methods and cognitive heuristics. For example, when options are assessed separately rather than jointly, people often use heuristics that lead to inconsistent choices (Tversky et al., 1990). These findings challenge the assumption in traditional economic theories that preferences are stable and consistent. Instead, they suggest that decision making is influenced by contextual factors, leading to suboptimal and inconsistent outcomes.

Biases and Heuristics Programme

The biases and heuristics programme, pioneered by Kahneman and Tversky, identifies systematic deviations from rationality where heuristics – mental shortcuts used to simplify decision making – often lead to biases. Key biases include:

• Availability Heuristic:Overestimating the likelihood of events based on their availability in memory, influenced by recent exposure or emotional impact.
• Representativeness Heuristic:Assessing the probability of an event based on its similarity to a prototype, often neglecting base rates and other relevant statistical information.
• Anchoring Effect:Relying too heavily on the first piece of information encountered (the “anchor”) when making decisions.

Kahneman and Tversky’s work demonstrates that these heuristics, while useful for simplifying complex decisions, frequently lead to consistent and predictable errors (Kahneman and Tversky, 1974). This body of research reveals that human judgment is often systematically flawed, leading to decision making inconsistencies that can have significant consequences in various contexts, from personal choices to policy making.

Gigerenzer’s Critique

Gerd Gigerenzer argues that heuristics can be adaptive tools enabling efficient decision making in complex environments. He criticises the biases and heuristics programme for underestimating the ecological rationality of heuristics – how well they perform in real-world settings. Gigerenzer contends that many of the so-called biases identified by Kahneman and Tversky are artefacts of experimental conditions rather than genuine flaws in human cognition (Gigerenzer, 2015 ; Luan et al., 2019).

Gigerenzer’s perspective suggests that human decision making is more robust and rational than the biases and heuristics programme depicts. He advocates for a “fast and frugal” approach, where simple heuristics are seen as effective strategies tailored to specific environmental structures. This approach underscores the context-dependent nature of decision making, arguing that what might appear as irrational in a laboratory setting could be highly adaptive in real-world scenarios.

The Role of Noise in Decision Making

Noise refers to the variability in human judgment that leads to different decisions under identical circumstances. As explored by Sibony, Sunstein, and Kahneman in their book Noise, this variability can stem from numerous sources, including different interpretations of information, personal mood variations, or even the time of day when a decision is made (Sibony et al., 2021). Unlike biases, which systematically skew decisions in one direction, noise introduces randomness and inconsistency.

Cognitive biases interact with noise, amplifying the challenges in achieving consistent decision making. Noise is essential for understanding why decision makers are not always consistent. It highlights that even without systematic biases, variability in judgments can lead to unpredictable and suboptimal outcomes. For instance, two judges might deliver different sentences for the same crime, or two doctors might suggest different treatments for the same condition, simply due to noise.

The implications of noise are profound. Beyond correcting biases, efforts to improve decision making must also address this inherent variability. Strategies to reduce noise include standardising decision processes, implementing checklists, and employing algorithms to ensure more consistent outcomes. Recognising and mitigating noise can lead to more reliable and equitable decisions in various contexts, from judicial settings to medical diagnoses and corporate strategies.

Foundational Research by Slovic, Fischhoff, and Lichtenstein

The early work of Paul Slovic, Baruch Fischhoff, and Sarah Lichtenstein is foundational to BDT. Their research on risk perception and preference reversals established a crucial understanding of how people perceive and respond to risk and uncertainty. Their studies demonstrated that people’s judgments about risks are often influenced by factors beyond objective probabilities, such as emotional reactions and framing (Slovic et al., 1980).

Slovic, Fischhoff, and Lichtenstein’s work complements that of Kahneman and Tversky, highlighting that decision making under uncertainty is fraught with biases and influenced by subjective perceptions. This body of research underscores the importance of context and presentation in shaping decisions. Their findings reveal that risk perception is not merely a matter of objective analysis but is deeply intertwined with cognitive and emotional factors.

Decision Making Inconsistencies: Insights and Applications

Implications from Research

Intertemporal Uncertainty

Intertemporal uncertainty refers to the unpredictability of future outcomes, affecting decisions that require trade-offs over time. Research by Laibson (1997) on hyperbolic discounting reveals that individuals often prefer immediate rewards over larger future rewards, leading to inconsistent and irrational decisions. This present bias can hinder long-term planning, resulting in choices that favour short-term gains at the expense of long-term benefits. Understanding intertemporal uncertainty is crucial for enhancing decision making in areas demanding long-term commitment and strategic foresight.

Uncertainty’s impact extends across various sectors, significantly influencing decision making in finance, healthcare, and public policy. Each sector faces distinct challenges that underscore the broader implications of uncertainty. For instance, financial markets may experience volatile investment decisions, healthcare can see variability in medical diagnoses, and public policy might witness short-sighted measures, all stemming from the inherent unpredictability of future outcomes. Understanding and managing this uncertainty is vital for promoting more consistent and rational decisions across these diverse fields.

Examples and Case Studies

Financial Markets

In financial markets, uncertainty can lead to volatile investment decisions. Investors often react to short-term market fluctuations, driven by biases such as overconfidence and loss aversion. This behaviour can result in inconsistent investment strategies and suboptimal portfolio performance. For instance, during market downturns, investors might panic and sell off assets, only to buy them back at higher prices when the market recovers. Standardising investment strategies and using decision support tools can help mitigate the effects of uncertainty and improve decision consistency.

Healthcare

In healthcare, uncertainty in diagnosing and treating diseases can lead to inconsistent medical decisions. Different doctors may provide varying diagnoses and treatment plans for the same patient, influenced by their individual biases and the inherent uncertainty in medical information. For example, two doctors might recommend different treatments for the same condition based on their personal experiences and interpretations of medical data. Standardising diagnostic procedures and implementing evidence-based guidelines can help reduce variability and improve consistency in medical decisions.

Public Policy

In public policy, uncertainty about the long-term effects of policies can lead to inconsistent decision making. Policymakers may favour immediate, visible benefits over long-term, less tangible outcomes, influenced by biases such as myopia and political pressures. For instance, policymakers might implement short-term measures to address economic issues, neglecting the long-term implications of their actions. Incorporating long-term impact assessments and evidence-based policy frameworks can help mitigate these biases and improve policy consistency.

Labour Supply of New York Cab Drivers: A Case Study

The research by Loewenstein, Camerer, Babcock, and Thaler on the labour supply of New York City cab drivers provides a concrete example of decision making inconsistencies. Their study found that cab drivers often decide when to quit based on daily income targets rather than considering overall profitability or efficiency (Camerer et al., 1997). This behaviour, driven by a cognitive bias known as the target-income hypothesis, leads to suboptimal decision making where drivers work longer hours on slow days and quit early on busy days.

This case study illustrates how cognitive biases can lead to inconsistent decision making. The drivers’ reliance on daily income targets instead of a more rational approach to maximise earnings over time demonstrates the pervasive impact of biases on decision behaviour. The study of cab drivers’ labour supply also highlights the role of framing effects in decision making. By reframing the decision from a daily target to a more holistic evaluation of earnings, drivers could make more consistent and profitable choices. This insight underscores the importance of context and framing in shaping decision outcomes.

Practical Implications and Strategies

Insights from Max Bazerman

Max Bazerman’s research significantly advances our understanding of why decision makers are not always consistent. He explores the concept of predictable surprises, where organisations fail to act on foreseeable issues due to cognitive biases and organisational inertia (Bazerman and Watkins, 2004). These biases lead to inconsistencies in decision making as decision makers often fail to anticipate and respond to predictable events, revealing the limits of human rationality. By recognising and mitigating these biases, organisations can reduce inconsistencies in decision making and improve outcomes.

Bazerman highlights that decision makers often succumb to overconfidence, bounded awareness, and an inability to consider alternative perspectives (Bazerman, 2006). These cognitive limitations contribute to erratic decisions by narrowing the decision making process. Addressing these limitations through structured analytic techniques and encouraging diverse viewpoints can help counteract biases, promoting more consistent decision making practices.

Climate Change as a Predictable Surprise

In his analysis of climate change as a “predictable surprise,” Bazerman illustrates how cognitive biases, organisational inertia, and political barriers prevent effective action. Despite clear scientific evidence and consensus, societal responses remain erratic. Cognitive biases such as positive illusions, egocentrism, and overly discounting the future contribute significantly to this failure (Bazerman, 2006). These biases cause decision makers to underestimate the severity of climate change and delay necessary actions, showcasing why decisions are not always consistent.

Positive illusions lead people to misjudge the severity of climate change and overestimate their ability to control its effects. Egocentrism causes nations to deflect responsibility onto others, while overly discounting the future results in a lack of urgent action. These cognitive distortions highlight the inconsistent nature of decision making processes in the face of global challenges. To address these issues, Bazerman suggests a multi-level approach encompassing cognitive, organisational, and political strategies, thereby promoting more consistent and effective decision making.

The Importance of Knowing When to Quit: Insights from Annie Duke

Annie Duke’s work in Quit underscores the importance of recognising when to abandon a failing course of action. Cognitive biases, such as loss aversion and the sunk cost fallacy, often prevent individuals from quitting even when it is the rational decision (Duke, 2022). These biases contribute to decision making inconsistencies as individuals and organisations persist in unproductive endeavours, highlighting why decision makers are not always consistent.

Duke’s insights emphasise that recognising the right moment to quit is crucial for consistent decision making. For instance, in business, acknowledging when a project is underperforming and reallocating resources to more promising opportunities can lead to better outcomes. However, biases cloud judgment, making timely and consistent decisions about when to quit challenging. By understanding and addressing these psychological barriers, decision makers can develop more rational strategies, thus enhancing the consistency and effectiveness of their decisions.

Organisational Change and Decision Making

Cognitive Biases and Organisational Change

The insights from Behavioural Decision Theory (BDT) have significant implications for behavioural change management within organisations. Cognitive biases and noise often lead to erratic decision making, hindering effective change management. Recognising and mitigating these biases and inconsistencies are crucial for establishing more reliable decision making processes.

Organisational change initiatives often fail due to cognitive biases such as status quo bias, anchoring, and confirmation bias. Employees and managers may resist change because they prefer familiar routines and practices. These biases contribute to inconsistent decision making, as the psychological underpinnings of resistance to change are not addressed. To overcome these biases, change leaders need to design interventions that consider the psychological factors influencing resistance. For example, framing change initiatives to highlight benefits and reduce perceived risks can help mitigate status quo bias. Providing clear, evidence-based information and encouraging open dialogue can reduce confirmation bias. By understanding and addressing these biases, organisations can improve the consistency and effectiveness of their change management efforts.

Reducing Noise in Organisational Decision Making

Noise in organisational decision making leads to unpredictable and inconsistent outcomes. Standardising decision processes, implementing decision support systems, and using algorithms can help reduce noise and enhance consistency. For instance, structured decision making frameworks ensure that all relevant factors are considered, thus reducing variability in decisions and promoting consistency. Incorporating feedback mechanisms and continuous improvement practices can help identify and mitigate sources of noise in decision making. By creating a culture of learning and adaptability, organisations can enhance their ability to respond to changing circumstances and make more consistent decisions.

Leveraging Behavioural Insights for Change Management

Behavioural insights can inform the design of change management interventions. For example, using nudges – small design changes that influence behaviour without restricting options – can encourage desired behaviours and reduce decision making inconsistencies. Setting default options that align with organisational goals, providing timely feedback, and using social norms to influence behaviour can enhance the effectiveness of change initiatives. Bazerman’s research on predictable surprises highlights the importance of proactive and preventative strategies in change management. By anticipating potential barriers and designing interventions that address cognitive, organisational, and political factors, organisations can increase the likelihood of successful change, thereby promoting more consistent decision making.

Actionable Recommen­dations

1. Implement Decision Pre-Mortems:Before finalising major decisions, conduct a pre-mortem session where team members imagine a scenario where the decision has failed. This technique helps identify potential pitfalls and biases that might have been overlooked, enhancing a more thorough and realistic evaluation of the decision making process.
2. Utilise Behavioural Analytics:Integrate behavioural analytics tools to monitor and analyse decision making patterns within the organisation. By understanding how decisions are made, where biases occur, and how noise affects outcomes, practitioners can develop targeted interventions to enhance decision consistency and effectiveness.
3. Design Flexible Decision Protocols:Create decision making protocols that allow for flexibility and adaptability in response to new information and changing circumstances. Encourage regular reviews and updates of decisions, ensuring that the organisation remains agile and can pivot, when necessary, based on the latest data and insights.
4. Promote Interdisciplinary Collaboration:Establish a culture of interdisciplinary collaboration to bring diverse perspectives into the decision making process. By including insights from different fields, such as psychology, economics, and sociology, organisations can mitigate individual biases and noise, leading to more balanced and informed decisions.

Conclusion

Understanding why decision makers are not always consistent requires examining the underlying cognitive processes and environmental factors influencing decisions. The foundational research in BDT, from preference reversals to the biases and heuristics programme and the role of noise, provides critical insights. The contrasting perspectives of researchers like Gigerenzer versus Kahneman and Tversky, as well as the significant contributions of Bazerman and Duke, enrich this understanding by highlighting the complexities and adaptive nature of human cognition. These insights are crucial for developing strategies to improve decision making consistency in various real-world contexts.

By integrating these diverse perspectives, we can better understand the mechanisms driving decision making inconsistencies and develop more effective interventions to enhance decision quality. Whether in personal choices, organisational strategies, or public policy, recognising and mitigating biases and noise can lead to more rational and consistent decisions, ultimately improving outcomes across various domains. The relevance of these insights for behavioural change management within organisations cannot be overstated. By applying behavioural science principles, organisations can design more effective change initiatives, reduce decision making variability, and create a more adaptable and resilient organisational culture.

Glossary of Key Terms

• Anchoring Effect:A bias where individuals rely too heavily on the first piece of information encountered when making decisions.
• Availability Heuristic:A bias where individuals overestimate the likelihood of events based on their availability in memory.
• Behavioural Decision Theory (BDT):A field of study that examines the psychological mechanisms behind human decision making, highlighting inconsistencies and biases.
• Cognitive Bias:Systematic patterns of deviation from norm or rationality in judgment, leading to illogical or erroneous decisions.
• Heuristic:Mental shortcuts or rules of thumb that simplify decision making but can lead to biases.
• Noise:Random variability in judgments that leads to different decisions under identical circumstances.
• Preference Reversal:The phenomenon where individuals switch their preference between two options depending on how they are evaluated.
• Predictable Surprise:Situations where organisations fail to act on foreseeable issues due to cognitive biases and organisational inertia.
• Representativeness Heuristic:A bias where individuals assess the probability of an event based on its similarity to a prototype.
• Target-Income Hypothesis:The tendency of individuals to set a specific income target and make decisions to meet that target, often leading to suboptimal outcomes.

 

References

Bazerman, M. H. (2006), Climate change as predictable surprise, Harvard Business Review, 84(10), pp. 110-116.

Bazerman, M. H. and D. A. Moore (2013), Judgment in Managerial Decision Making, Hoboken: Wiley

Bazerman, M. H., E. A. Tenbrunsel, and K. A. Wade-Benzoni (1998), Negotiating with yourself and losing: Making decisions with competing internal preferences, Academy of Management Review, 23(2), pp. 225-241.

Bazerman, M. H., and Watkins, M. D. (2004), Predictable Surprises: The Disasters You Should Have Seen Coming, Boston: Harvard Business School Press

Camerer, C. F., L. Babcock, L., G. Loewenstein, and R. Thaler (1997), Labor supply of New York City cabdrivers: One day at a time, The Quarterly Journal of Economics, 112(2), pp. 407-441.

Duke, A. (2022), Quit: The Power of Knowing When to Walk Away, Penguin Random House

Gigerenzer, G. (2015), Towards a rational theory of heuristics, in: W. J. Maule and R. J. Filbeck (Eds.), Simple Heuristics in a Complex World (pp. 45-59), Palgrave Macmillan

Kahneman, D., and Tversky, A. (1974), Judgment under uncertainty: Heuristics and biases, Science, 185(4157), pp. 1124-1131.

Laibson, D. (1997), Golden eggs and hyperbolic discounting, The Quarterly Journal of Economics, 112(2), pp. 443-477.

Luan, S., Reb, J., and G. Gigerenzer (2019), Ecological rationality: Fast-and-frugal heuristics for managerial decision making under uncertainty, Academy of Management Journal, 62(6), 1735-1759.

Milkman, K. L., D. Chugh, and M. H. Bazerman (2009), How can decision making be improved?, Perspectives on Psychological Science, 4(4), pp. 379-383.

Sibony, O., C. R. Sunstein, and D. Kahneman (2021), Noise: A Flaw in Human Judgment, New York: Little, Brown Spark

Slovic, P., B. Fischhoff, and S. Lichtenstein (1980), Facts and Fears: Understanding Perceived Risk, University of Oregon

Tversky, A., P. Slovic, and D. Kahneman (1990), The causes of preference reversal, The American Economic Review, 80(1), pp. 204-217.