Blog: Introduction to decision making
Understanding Human Behavior
Author: Krishane Patel | 04/01/2025Background
Picture this: You're scrolling through social media and see everyone buying a
new gadget. Despite not needing it, you feel an inexplicable urge to buy one
too. Or perhaps you've held onto a losing investment because selling would make
the loss "real". Maybe you've found yourself bulk-buying toilet paper during a
pandemic, even though you knew supply chains were stable.
Traditional economic theory depicted humans as purely rational beings - homo
economicus (Levitt & List, 2008) - who carefully calculate every
decision with perfect logic. This idealised version of humanity would never
panic buy, fall for marketing tricks, or make emotional investment decisions.
According to this view, we're all walking calculators operating on four key
principles:
Rationality
We make decisions logically and objectively to maximise our well-being.
Consistent preferences
We have well-defined preferences that remain stable over time.
Utility maximization
We seek to maximise our well-being or satisfaction.
Perfect information
We have complete and accurate knowledge of all factors that will influence the decision.
But this model breaks down when confronted with real human behaviour. Take the equity premium puzzle (Mehra & Prescott, 1985) - one of economics' most persistent mysteries. Despite stocks consistently outperforming bonds by 5-8% over the long term, many sophisticated investors still prefer bonds. From a purely rational perspective, this behaviour makes little sense, people seem to favour the option with lower returns. Yet this puzzle persists across cultures, time periods, and levels of financial literacy.
Enter Herbert Simon's revolutionary concept of bounded rationality (1957). Simon proposed that our decision-making isn't unlimited - it's constrained by our cognitive capacity, time, and available information. We don't optimise; we satisfice - finding solutions that are "good enough" given our limitations. Why? Because our brains aren't the logical computing machines we imagine them to be. Instead, they're sophisticated pattern-recognition systems shaped by millions of years of evolution to make quick, efficient decisions - sometimes at the expense of accuracy. This insight transformed our understanding of human behaviour and laid the groundwork for modern behavioural science.
What is behavioural science?
Behavioural science is a multidisciplinary approach that studies human behaviour across fields including cognitive science, psychology, neuroscience, economics, anthropology, sociology and so forth. It draws on two fundamental approaches:
Normative theory
Theories that explain what people should do.
Descriptive theory
Theories that describe what people actually do.
What does this mean for how we make decisions?
Decision making is what is known as an executive function, which requires lower level processes to aggregate and build to an output. This means our decisions are subject to the base level functioning of information processing in the brain. Information processing theory explains how the human brain handles information, leading to both efficient decision-making and the potential for cognitive biases. This theory suggests that cognitive processes can be understood in terms of how information is perceived, encoded, processed as well as storage and retrieved, akin to a computer system:
- Perception - the sensory inputs into the system
- Encoding - our inputs are translated into neurosensory information.
- Processing - neurosensory programs are enacted on the information
- Storage & Retrieval - information is stored to be retrieved for later use.
Why is information processing so important?
Every second, your brain is bombarded by all types of sensory information, what
you
can hear, see, touch, smell as well as the temperature, tactile information and
so
much more. Yet you're consciously
aware of only a narrow band of this. Consider the
cocktail party effect (Bronkhorst, 2000) - your ability to converse
with
someone in front of you and yet instantly tune
into someone mentioning your name across a crowded room, even when you weren't
consciously listening to their conversation. This demonstrates the efficiency of
our
attention systems. We're constantly filtering, prioritizing, and
processing information, much of it outside our conscious awareness.
But this same filtering system that lets us function in a complex world can also
create blind spots. We might miss crucial information because of how our
information
processing works, we may weigh something more or less just by virtue of how that
information is understood by the brain.
These tendencies become particularly important in high-stakes decisions anf
speaks
to our brains ability
to simplify and filter information to make sense of the world. Which is why
we've
evolved
to find tradeoffs between speed and accuracy.
For instance, see the six shapes below. These can be understood in terms of
shapes
with three circles and three squares, as well as colours with two green, two
navy
and two orange shapes. Depending on the context, we might categorise the
different
shapes and colours differently depending on how they are ordered and arranged.
When
we see like colours together our brain starts to emphasise the colour attributes
more than the shapes, leading us to easily discern three pairs of colours. When
shapes are grouped together, our brains are better at grouping different shapes
together. This is a very simple example of how important information processing
is
expressed through the Gestalt theory of psychology (Köhler, 1967). These basic
information processing functions underlie many of the decision processes we use.
How do such basic information processing tendencies manifest and affect our decision making?
Our decisions are heavily influenced by these fundamental processes, as each step can influence how information is valued and used, leading to different outcomes. Where these mechanistic processes influence our decisions, we can observe the outcomes. For instance, our perceptual system is highly sensitive to salience (Schneider & Shiffrin, 1977). Through bottom-up, memory free mechanisms we can create a quick and efficient perceptual system that can identify potential threats and opportunities. Evolutionary biology has shaped many poisonous plants and animals to be highly salient colouring as an early indicator of risk (Stankowich, Caro & Cox, 2011). Therefore salient stimuli are more prominent in our visual system and as a result we value these options greater than less salient options. This means that salience can also be used to influence our decisions, as exemplified in research by Gofman, Moskowitz and Mets (2010), who found that the visually salient packaging increased purchase intent and product recall.
There are a lots of different biases that can influence our decision-making process, these are known as cognitive biases, each describe some aspect of how we perceive and process information. We also have what are known as heuristics. Heuristics are mental shortcuts or rules-of-thumbs. We use heuristics as a result of limited information through filtration (where our brain is filtering out information to help us make sense) or processing (where our brains are preoccupied processing other information). Heuristics help to solve for the speed-accuracy tradeoff (Gigerenzer, 2008). Below are some heuristics and biases prevalent within the financial markets.
Confirmation bias
We tend to search for, interpret, and remember information that confirms our beliefs.
Overconfidence bias
We often overestimate our performance or skill.
Anchoring Bias
We seek to maximise our well-being or satisfaction.
Herd behaviour
We have a tendency to follow and mimic the actions of a larger group.
Loss aversion
We overweight the negative value of losses greater than the positive value of gains.
Recency bias
The more recent an event, the more likely it is that we will remember it.
Scarcity heuristic
The more scarce something is the more we value it
Social proof
We look to others for cues and guidance on how to behave
Herd behaviour is an example that seems reasonable in one context but bizarre in another. Herd behaviour is the tendency to follow the actions of others, it stems from the social proof principle (see Cialdini 2009). Most decisions are made under uncertainty, and we always seek to minimise uncertainty. One of these approaches is looking to understand what others are doing. As the signal grows stronger (more people take part in this behaviour) the stronger we feel the pull. This can often lead to a herd mentality where simple decision rules can create emergent macro-level outcomes. Herd mentality has been shown to be a substantial factor regarding cryptocurrencies bubbles (Nicolas, 2022).
The anchoring heuristic is a heuristic in which we
anchor
on the initial information presented. For example Kahneman and Tversky (1974)
gave
two groups the following sums, one group saw an ascending sequence of
multiplications:
1 x 2 x 3
x 4
x 5 x 6 x 7 x
8
whilst another group saw a descending sequence of multiplications
8 x 7 x
6 x
5 x 4 x 3 x 2 x
1
Each group was only given 5 seconds to compute the answer, however the results
are
strikingly difference, the ascending group saw a median of 512, whilst the
descending group observed a median of 2250 (Note the answer is 40,320). This is
because both group anchored on the first few numbers in the sequence due to the
time
constraints. What causes the anchoring and adjustment? It stems from information
processing to tradeoff resources for a speed-accuracy tradeoff (Lieder,
Griffiths,
Huys & Goodman, 2018).
Why This Matters
Understanding information processing and how humans make decisions is influential
for all businesses. It is important to understand that customers are people too, and
must navigate a complex world bombarded by information.
Behavioural science, in its descriptive theory, can be used to understand how people
make decisions and the processes involved. Through the use of a behavioural science
lens, we can design better products and services that reflect
human cognition and go with the grain of how people actually behave. From financial
products, technology, communications, even public policy, all can benefit from
understanding the nuance in human behaviour, to deliver on key impacts such as:
- Enhance product development by aligning with how customers truly make decisions.
- Develop pricing strategies rooted in psychological principles.
- Design communications to drive customer behaviour and promote good customer outcomes.
- Implement risk assessment and management systems informed by behavioural insights.
In upcoming posts, we will explore each of these areas in greater depth, uncovering practical applications and offering actionable strategies. Through real-world case studies, we will showcase how understanding behavioural science has led to groundbreaking solutions. Additionally, we will provide frameworks to support better outcomes and empower you to implement these insights effectively.
References
- Levitt, S. D., & List, J. A. (2008). Homo economicus evolves. Science, 319(5865), 909-910.
- Simon, H. (1957). A behavioural model of rational choice. Models of man, social and rational: Mathematical essays on rational human behaviour in a social setting, 6(1), 241-260.
- Financial Times. (2022). Boris Johnson prepares to call in army as panic buying drains UK petrol pumps. Financial Times.
- Gneezy, U., & Rustichini, A. (2000). A fine is a price. The journal of legal studies, 29(1), 1-17.
- Mehra, R. & Prescott, E. C. (1985). The equity premium: A puzzle. Journal of Monetary Economics. 15 (2): 145–161.
- Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in dividends? The American Economic Review, 71(3), 421-436.
- Bronkhorst, Adelbert W. (2000). "The Cocktail Party Phenomenon: A Review on Speech Intelligibility in Multiple-Talker Conditions". Acta Acustica United with Acustica. 86: 117–128.
- Cialdini, R. B. (2009). Influence: Science and practice (Vol. 4, pp. 51-96). Boston: Pearson education.
- Nicolas, M. L. (2022). Estimating a model of herding behaviour on social networks. Physica A: Statistical Mechanics and its Applications, 604, 127884.
- Lieder, F., Griffiths, T. L., M. Huys, Q. J., & Goodman, N. D. (2018). Empirical evidence for resource-rational anchoring and adjustment. Psychonomic Bulletin & Review, 25, 775-784.
- Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292.
- Walasek, L., & Stewart, N. (2015). How to make loss aversion disappear and reverse: tests of the decision by sampling origin of loss aversion. Journal of experimental psychology: general, 144(1), 7-11.
- Köhler, W. (1967). Gestalt psychology. Psychologische forschung, 31(1), XVIII-XXX.
- Schneider, W. & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review. 84 (1): 1–66. doi:10.1037/0033-295x.84.1.1
- Gigerenzer, G. (2008). Why heuristics work. Perspectives on psychological science, 3(1), 20-29.