The Promethean Guide to Human Irrationality: Learning from Daniel Kahneman 

May 2024

In 2011, during my postgraduate studies in finance, I encountered prospect theory in a behavioural finance class. This was also the year when Daniel Kahneman's groundbreaking book, Thinking, Fast and Slow, was published. His work not only shaped my academic understanding of human decision-making but also resonated deeply with me on a personal level, influencing various aspects of my personal and professional life.

As I ventured into my role as a corporate finance advisor, the principles of behavioural economics, particularly those of Daniel Kahneman, began to reveal their profound relevance. It became clear to me that an advisor's role is not to predict market movements—an inherently uncertain pursuit—but to guide clients, helping them navigate and exploit biases in decision-making. Kahneman's teachings not only encouraged me to reflect on the mental shortcuts and fallacies that often cloud decision-making in both my clients and myself, but also directly influenced my approach to advising.

Perhaps most transformative was Kahneman's quiet insistence on the power of doubt. He taught me to question my assumptions and judgments, reminding me that human reason, left to its own devices, is prone to systematic errors. His work, just like Prometheus' gifting mankind with fire, was the arms and ammunition with which to fight against the flaws of our thinking. In exposing such fallacies in human cognition, Kahneman gave us ways toward better decision-making, not just personally but at levels of society. That becomes a gift nothing can measure.

We are sad to mention that we recently lost a towering figure in psychology and economics. Daniel Kahneman was born in Tel Aviv in 1934 and died at age 90, leaving behind a fantastic legacy- a lifetime in which he restructured how we think. With his longtime collaborator Amos Tversky, Kahneman spent decades attacking that economic model, which held that people were rational agents. The two showed how the endemic breaches of rationality underpin a great deal of human decision-making. For these seminal contributions, in 2002, Kahneman received the Nobel Prize in Economic Sciences for having integrated aspects of psychological reasoning into studies of human judgment and decision-making under uncertainty.

Kahneman's intellectual journey began with research into visual perception and attention, but his curiosity soon stretched to include judgmental bias, decision-making, and well-being. Concepts such as anchoring bias and prospect theory are characteristics of his work that have enriched the social sciences and disciplines as varied as marketing, policy-making, and organisational behaviour.

The study of Kahneman's works has been nothing short of transformational. His insights have not only made me more aware of myself but have also deepened my understanding of people and their situations. Rather than a collection of academic theories, Kahneman's work has been a guiding light in my quest to make sense of the world and to approach it with greater empathy and clarity.

Even in his absence, Kahneman's contribution will remain there. They signal to us that at the intersection of complexity and perfection in human decisions, there is tremendous value in understanding biases to mitigate their impacts. In such a thinking pattern, the contribution of Kahneman was an encouragement to all those who are committed to 'good' thinking; it presents us with another challenge to show humbleness in judgments, consciousness in reasoning, and increasing curiosity about subtleties connected with the mechanism of the human mind.

Some of the essential notions and concepts developed in the oeuvre are listed below:

Human Irrationality:

Daniel Kahneman discussed human decision-making and said that human behaviour is pretty irrational. Conventionally, we perceive people as rational actors who make decisions based on complete and perfect information. However, Kahneman's research reveals that irrationality strongly influences our choices, showing how cognitive biases and emotional responses usually lead us away from logical reasoning. This gap between actual behaviour and the assumptions of economic models brings out a complex and unpredictable reality.

His work on human irrationality outlines how biases, heuristics, and cognitive errors can result in imperfect decisions in many areas. Concepts like the endowment effect and loss aversion in behavioural economics reveal how ownership inflates perceived value and how people fear losses disproportionately more than value gains.

Anchoring biases results in misguided diagnoses, and overconfidence in treatment decisions often leads to unnecessary procedures. The legal system shows potential problems of variability of judgment and jury decisions driven by heuristics; these undermine fairness.

Marketing takes advantage of such irrational tendencies through various techniques, like framing effects and the decoy effect, to sway consumer choices. Public policy and education face similar biases: default bias in retirement savings, availability bias in risk perception, and vivid but rare events warp people's judgment. In technology, people either overtrust or undertrust algorithms, largely disregarding evidence regarding the algorithms' reliability. Overconfidence and optimism bias also extend to the workplace decisions of hiring and project planning. The peak-end rule and duration neglect, two memory distortions, appear in personal life. Mental accounting and the sunk cost fallacy are some concepts in financial decision-making that result in inconsistent and irrational behaviours. These examples show how deeply ingrained these cognitive biases shape human behaviour across different domains.

Prospect Theory: The Emotional Heart of Decision-Making

In the mid-1970s, the psychologist Daniel Kahneman and his collaborator Amos Tversky changed our understanding of decision-making with a new prospect theory. This theory demonstrates that people assign a much greater emotional weight to losses than gains. For example, the agony of losing $50 is much more intense than the delight of winning the same sum. This is why people often seek options presenting certainty, even if those options carry a lower expected value.

Kahneman noted that gains and losses have immediate emotional responses that may override logical reasoning. One essential concept from prospect theory is loss aversion, which demonstrates how the fear of loss might lead us to make decisions that sometimes result in our forgoing potential gains. Kahneman once said, "The pain of losing appears to be greater than the pleasure of winning by a factor of about two to one."

The insights from prospect theory yield essential lessons that can be applied in both personal and professional contexts. One key lesson is the power of framing: how a decision is framed dramatically influences individuals' choices. A scenario framed around potential gains is typically more appealing than one focused on potential losses, even if the outcomes are identical. This understanding helps us approach decisions with more excellent care, guarding against being swayed by external influences.

Loss aversion really comes into its own, especially in negotiation. Highlighting the gains but minimising the perceived risks of an offer is a highly effective means of influencing others' choices. Conversely, knowing how others frame choices this way may help us resist manipulation.

Perhaps Kahneman's most profound contribution is identifying the singular role of awareness. His research presses us to become more reflective about our prejudices and question intuition, subverting preconceptions to reach fully informed decisions based on a more complete comprehension of our options and possible consequences.

Two Systems of Thinking: System 1 & System 2

This is a bold outline by Daniel Kahneman to investigate the duality of the systems of thinking to understand the intricacies of our minds and decision-making processes. He teases out two systems, System 1 and System 2, working together to build and shape how we interpret experiences and react to the world around us.

System 1 is the intuitive mind- a mental autopilot that enables us to make swift, instinctive judgments without thinking consciously. It is fast, emotional, and always on, processing information based on patterns we have learned from experience. This system controls our immediate reactions, such as fear of a spider or recognising a friend in a crowd. While this speed is a strength, it is also a weakness; System 1 favours coherence over accuracy. It may result in a hasty generalisation or decision based on cognitive biases such as the halo effect. We allow our overall impression of a person to influence our perception of the person's specific traits. The affect heuristic is a judgment of good or bad results from how one feels about something rather than on any evidence. On the other hand, System 2 represents the deliberative mind. It has its thinking analytically, with attention and conscious processing of information, such that it solves problems or plans long-range plans.

Additionally, this was like some quality system, analysing the majority of impulses of System 1 to make a reasonable judgment in System 2; lethargic, it simply does not want to commit itself unless it's necessary, working with "low effort mode". This will quickly lead to accepting the shortcuts provided by System 1 without questioning the validity of such shortcuts. Using System 2 for extended periods can also exhaust our mental resources and make us give in to impulsive decisions when tired or stressed.

The interplay between these two systems creates a dynamic tension that influences our behaviour. While System 1 continuously generates thoughts and feelings, System 2 intervenes only when necessary to address a problem. This relationship can result in oversimplified narratives when System 1 faces complexity. When confronted with challenging questions, System 1 may unconsciously substitute them with simpler ones, leading us to base our judgments on emotional impressions rather than thorough analysis.

Knowing this interaction is instructive in the most profound ways for decision-making. When we know our biases—our proclivity for loss aversion or overconfidence—we learn to stop and engage System 2 in more reflective deliberation. Let us teach ourselves to adopt this slow thinking, especially when substantial life choices are at stake, whether that means retirement or inner social conflict. Significantly, how we frame our questions or decisions will vastly affect the functioning of our minds, reminding us of the power of perspective in shaping our judgments.

Kahneman's insights beckon us to reflect on the mechanics of our thinking and cultivate a more mindful approach to decision-making in the face of uncertainty and complexity. We can achieve a balance by recognising the strengths and weaknesses of both systems and working toward making more intelligent choices.

What You See Is All There Is (WYSIATI): The Illusion of Completeness in Human Judgment

What You See Is All There Is" brilliantly encapsulates one of the basic cognitive phenomena guiding better judgment and decision-making. This principle so eloquently underlines our natural tendency to rely exclusively on whatever information is available to us without considering the broader context that may be outside our sight or unknown. This view of how the concept works rests at the centre: System 1, the intuitive and automatic component of our thinking, operates by creating a coherent narrative from fragmented evidence and is prone to constructing intuitively appealing scenarios that often have little basis.

WYSIATI shows that the mind tries to simplify and create order, believing in what comes immediately before us. System 1 is a doting joiner of dots, creating causes and inventing events. Hence, it is a powerful generator of stories, almost always dangerously wrong or incomplete.

Kahneman now explains the operation behind WYSIATI: System 1 is designed for speed and low effort. Primarily, this is achieved at a cost in comprehension and realism. For instance, we overgeneralise when we experience a couple of cases of bad service in a restaurant and assume the restaurant always provides terrible service or see three people agree on something and think it must be true. These promiscuous abstractions of judgment lead us to make far-reaching generalisations based on little data with a few observations and render us vulnerable to error when the evidence is partial or nonrepresentative.

The mistakes that flow from WYSIATI are diverse and prolific. The Law of Small Numbers is a familiar illustration of our intuitive shortcomings regarding statistics, especially about small samples. We often generalise from the particular to the whole, jumping illogically from an individual case or a small sample to a general conclusion with too little evidence. Equally common is the false attribution of causal links between random events; System 1 is set to detect causes even when the correlation is merely accidental. The problem is that such a tendency fosters an illusion of understanding, where we create coherent retrospective narratives about past events by over-simplifying situations and excluding chance and luck from them.

Hindsight bias reinforces these problems since we tend to revise our recollection of what we knew to reflect the outcome and often further persuade ourselves that we "knew it all along." This produces a distorted judgment perception in decision-making because we are too usually guided by outcomes rather than reasons. Things are not made any easier by confirmation bias, which WYSIATI induces in us to search for confirming evidence to our already-held beliefs while giving little consideration to possible information that runs counter. Such reinforcement of a coherent narrative denies the real-world complexities and inconsistencies.

Another effect of WYSIATI is overconfidence. The illusion of understanding fosters overconfidence in our judgment and predictions. We are too often blind to the looseness of that connection and feel no discomfort when our narratives clash. As a result, overconfidence comes about because of an inability to consider what we don't know, which makes our conclusion complete and reliable. Another consequence of this cognitive bias is over-optimism in planning and overestimation in forecasting because it only pictures best-case scenarios when making plans for the future, underestimating obstacles and completely neglecting the associated risks.

Central to WYSIATI is System 1's quest for coherence. This drive leads us to focus on making sense of what we know while downplaying the unknown. The capacity to integrate whatever evidence is available into an uninterrupted narrative is precisely what can make that narrative flawed or incomplete. This very preference for simplicity explains why conspiracy theories, prejudices, and stereotypes can assume such convincing strength. A conspiracy theory offers a simple, overall explanation for complicated events, which agrees with our desire to understand. Similarly, prejudices prosper in a no man's land of incomplete information, where System 1 can leap to simplistic conclusions from the limited data at hand. The costs of WYSIATI in decision-making can be large and significant. When we lean too heavily on System 1 thinking, we risk making poor choices based on incomplete information and biased reasoning. For instance, in the business world, decisions based on incomplete data could result in much bigger mistakes, and thus, better contextualised thinking and decision-making with broader considerations of evidence are needed to account for the complexity of our world.

Noise in Judgment

Daniel Kahneman introduces the concept of noise in his last book, "Noise: A Flaw in Human Judgement"; one not that well-recognised but critically undermines the accuracy of our decisions. This refers to the gratuitous variability of judgment, which should be uniform. Unlike bias, a one-way systematic deviation, noise is random and thus unpredictable. As Kahneman so astutely puts it, "Wherever there is judgment, there is noise, and there is more of it than you think," or the almost complete permeability in most decision-making processes. Noise basically manifests itself along two dimensions: the inconsistency within one individual's judgments on different occasions and the variations from one individual judgment to another judging the same circumstance. For example, an underwriter might rate the same risk more optimistically at one moment than when she has just incurred a loss. This problem of occasion noise occurs because judges can hand out sentences for similar crimes that range widely owing to individual propensities. Similarly, in managers' standard evaluations of applicants, significant discrepancies arise from each individual tendency to weigh the criteria differently or their patterns and combinations; the most frequent manifestation is pattern noise. This adds up to individual variability, which Kahneman calls system noise; the result is significant inconsistencies at the institutional level that can lead to unpredictable and often unfair outcomes in such fields as health and employment.

Kahneman likens human judgment to measurement in the physical sciences. Just as physical measurement can be subject to error, human judgment can involve two flaws: bias, a systematic error, and noise, random variability. Whereas bias has been prominent in behavioural economics and psychology discussions due to its connection with prejudice, noise has been neglected, being less salient and more resistant to change. However, Kahneman emphasises its importance by stating that the total inaccuracy of a judgment is the sum of the squared bias and squared noise, which puts emphasis on the role of noise in decision-making errors.

Noise is more than a theoretical problem; it causes significant financial and social costs. For example, the lack of homogeneous risk evaluation in the insurance industry leads to pricing that can erode profitability and even fairness. In the criminal justice system, such unequal sentencing undermines public confidence and perpetuates unfairness. The consequences of noise do not stop at economics but also introduce ethical issues, such as inequality, which directly question the legitimacy of institutions.

Kahneman compares human judgment with the reliability of algorithms, which consistently deliver superior outcomes in predictive tasks. Simple statistical models, free from noise, have been found to yield more accurate results. Algorithms exhibit a level of consistency that human judgments can rarely match, with the potential to serve as a benchmark for precise and consistent decision-making. While human intuition and contextual awareness have their merits, noise inevitably clouds them.

To understand and decrease noise, Kahneman points out three primary sources: occasion noise, level noise, and pattern noise. Occasionally, noise emanates from transient factors, like the time of day or emotional state; level noise consists of systematic differences in judgment between individuals; pattern noise issues from unique ways different people weigh various factors when making judgments. His concept in managing the problems described is "decision hygiene," a disciplined approach to raising the quality of judgment. In the same vein that handwashing prevents the flu, decision hygiene attempts to reduce errors by advocating practices that promote the independence of judgment, decomposing complex choices into more minor judgments, and introducing a break before intuition.

While the structures and algorithms of formal decision-making procedures are noise-free, Kahneman recognises the value of intuition, especially in complicated or subtle situations. Yet, he warns against trusting one's gut feeling too much in cases without proper data analysis. The key is to balance intuitive insight and structured methodology; removing all noise could be expensive and even demoralise decision-makers.

Kahneman also draws on the work of Philip Tetlock, who researched "super forecasters," individuals with extraordinary forecasting abilities. Such insights shed light on the possibilities of making better decisions in any field, with an acknowledgement of the complexity involved in human judgment. In the final analysis, through understanding the hidden nature of noise and striving for a more subtle approach to decision-making, we can strive toward more just and fair results at the individual and institutional levels.

Well-being in Policy and Life

Recently, Kahneman has discussed well-being as a significant metric for measuring policies and decisions. He challenges traditional economic metrics such as GDP and unemployment rates for a change in outlook. According to Kahneman, policies should be judged based on how they affect people and their quality of life. This has been simplified into a single and profound insight: we must make an active choice to know how a policy affects the individual's well-being

This is essential to Kahneman's distinction between experienced utility and decision utility. Experienced utility is the real-time pleasure or pain individuals experience in an event, whereas decision utility is determined by their anticipation or memory of the event. This difference arose from research showing that people's willpeople'sto pay to avoid pain does not relate to the length or severity of their suffering. For example, a patient receiving a medical procedure may recall the experience as less painful if it ended positively, even though the overall experience was highly uncomfortable. This phenomenon is called the peak-end rule, and it illustrates how our memories distort reality, sometimes causing us to base our decisions on predicted memories rather than actual experiences. The peak-end rule presents a paradox: though we strive to prolong positive experiences, it is our memories—coloured by the most intense and final moments—that determine how much we enjoy the experiences. This "tyranny of m"mory" informs" our decisions about everything from vacations to career choices and often favours good stories rather than actual contributions to sustained well-being. Kahneman recognises how hard it is to work around this cognitive bias: individual experiences might be temporary, but our memories of them last and are vital to our long-term satisfaction.

In addition, Kahneman draws an essential distinction between two aspects of well-being: experienced happiness and life satisfaction. Experienced happiness consists of moment-to-moment experiences of joy, contentment, or pleasure. These are very closely tied to immediate circumstances, such as being with social others or engaging in enjoyable activities. On the other hand, life satisfaction is a more reflective judgment of one's life, including considerations of accomplishments, meaning, and a sense of fulfilment. It depends significantly on long-term goals, social expectations, and personal values. In making these distinctions, Kahneman has illuminated the complexity of well-being and encouraged a deeper understanding of how we evaluate our lives and the policies that govern them.

A Transformative Legacy

Kahneman also has profound insights into the intersection of artificial intelligence and human cognition, pointing to the current systems' successes and limitations. He likens AI to the process of human cognition, with deep learning similar to System 1 thinking. This form of cognition is highly adept at recognising patterns and predicting events but lacks the reasoning and causal understanding that marks System 2. This fundamental limitation prohibits AI from engaging in tasks requiring more profound cognitive handling.

However, deep learning differs from human intelligence as it requires giant datasets and cannot be learned with few examples. Kahneman finally pointed out that the key to successful AI is moving beyond correlation toward causality and meaningful representation. Kahneman also questioned whether the collaboration of humans and robots in the future could be a lasting concept, as really advanced machines will not need human input. He also acknowledges that modelling human behaviour is complex.

The other critical challenge is explainability. Kahneman noted that the inability of AI systems to explain themselves erodes trust. He believed that, instead of searching for truth, AI should construct a plausible and relevant story that can help humans understand it. This fits with humans' tendency to trust a good story.

Kahneman was a beacon of light to help navigate the intricacies of human behaviour. By exposing the cognitive shortcuts and emotional responses that form the basis for many decisions, he not only transformed the fields of economics and psychology but also gave practical means to enhance our lives. His teachings remind us that while human irrationality is inevitable, understanding and addressing it can lead to better outcomes in both personal and professional contexts.

For me, Kahneman's idKahneman's seen transformativtransformative-they'veself-awareness shaped how I approach decisions and reminded me of the immense power of perspective. As I reflect on his legacy, I'm struck by the enduring relevance of his work: the study of how we think about thinking.