Show people two photographs of unfamiliar political candidates for just a tenth of a second and ask which person looks more competent. Surprisingly, the candidate they choose is more likely to have won the actual election than chance alone would predict. Now ask a second group to take their time and think carefully before making the same judgment. Their predictions are actually worse. Most of their supposedly considered judgments still come from the initial impression, and the additional reasoning contributes little or nothing to predicting the result (Ballew & Todorov, 2007, pp. 17949–17950).
This finding challenges two common assumptions about human decision-making. The first is that mental shortcuts, or heuristics, are simply poor thinking that can be improved by taking more time. The second is that these shortcuts are generally reliable because they are fast and efficient. Neither is quite right. A heuristic can work remarkably well in one situation and lead to serious mistakes in another.
Essay 1 introduced an agent that made decisions by searching through a limited set of options. This essay takes the next step. What happens when the information an agent uses to make decisions is itself filtered through a mental shortcut? And how can we represent that shortcut in an agent-based model (ABM) in a way that produces specific, testable behaviour?
Theory
Tversky and Kahneman (1974) identified several common shortcuts people use when making judgments under uncertainty. Three are particularly important:
- Availability: judging how likely something is based on how easily examples come to mind. A recent terrorist attack, for example, may make terrorism seem more likely even if the underlying threat has not increased.
- Representativeness: judging something based on how closely it resembles a familiar pattern or stereotype. A political candidate who looks and speaks like a typical party member may be assumed to share that party’s positions.
- Anchoring: relying too heavily on an initial figure or piece of information, even when later evidence suggests it should be adjusted.
Their central argument was that these shortcuts are often useful, but can also produce predictable and systematic errors.
Gigerenzer (2008) offers an important qualification. He argues that heuristics work precisely because they ignore some available information. In uncertain environments, trying to process everything can sometimes produce worse decisions than using a simple rule.
One example is the 1/N rule for investing: divide your money equally across the available investment options. Harry Markowitz, who developed an influential mathematical method for optimising investment portfolios, reportedly used this much simpler approach for his own retirement savings. In tests comparing twelve optimisation strategies across seven real investment problems, none consistently outperformed the equal-allocation rule. The more complex methods were too closely fitted to the historical data and performed poorly when circumstances changed (Gigerenzer, 2008, pp. 22–23).
The broader point is that collecting and processing more information does not automatically produce better decisions. Sometimes, a simpler rule is more robust.
Gigerenzer also makes a methodological point that is especially relevant for ABMs. Calling a behaviour “availability bias” or “representativeness” does not yet explain how it works. To model a heuristic, we need to specify what information the agent pays attention to, how that information is processed, and how it affects the agent’s decisions.
Political Mechanism
Research on political behaviour shows that heuristics can help people make good decisions with limited information. But it also shows that the same shortcuts can lead them badly astray.
Ballew and Todorov’s findings about facial judgments were confirmed in a further test before the 2006 US elections. Judgments made two weeks before voting predicted 68.6% of gubernatorial races and 72.4% of Senate races, independently of whether a candidate was already in office (2007, p. 17951).
Lupia (1994) found a similar pattern in a referendum on insurance reform in California. Voters who knew little about the details of the proposals, but knew which position the insurance industry supported, made almost the same choices as voters who understood the proposals themselves. On Proposition 100, both groups recorded 53% voting yes. Among voters who lacked both kinds of information, only 27% voted yes (p. 71).
The implication is important for policy analysis: people do not always need a detailed understanding of an issue to make an informed choice. If they recognise a trustworthy and relevant signal, such as who supports a proposal, they may be able to use that signal as a shortcut.
However, this only works if the signal is reliable.
Lau and Redlawsk (2001) examined how 657 people used heuristics in a simulated election. Rather than simply asking participants how they decided, the researchers recorded which information they actually looked at.
Almost everyone relied on shortcuts, but the consequences differed sharply between groups. Among politically knowledgeable participants, extensive use of heuristics increased the probability of making the correct voting choice by around 20 percentage points. Among less knowledgeable participants, it reduced that probability by around 21 percentage points (pp. 962–965).
Even knowledgeable voters could be misled when familiar signals stopped being reliable. When the researchers introduced a candidate whose positions contradicted the usual stereotypes associated with their political party, the probability of a correct choice among sophisticated users of the party heuristic fell from 0.81 to 0.58 (pp. 964–966).
The lesson is that a shortcut is only as useful as the conditions in which it is applied. A rule that works in a familiar political environment can become misleading when that environment changes.
Kuklinski and colleagues (2000) identified another problem: people can be confidently misinformed. In a survey of 1,160 people in Illinois, respondents were asked factual questions about welfare. Among those who substantially overestimated the number of welfare recipients, 74% were very or fairly confident in their answers. Among those who answered correctly, only 47% expressed the same confidence (pp. 798–801).
This matters because a heuristic does not necessarily correct inaccurate information. If someone starts with the wrong understanding of a situation, a mental shortcut may reinforce that mistake rather than resolve it.
Nor are these problems limited to ordinary voters. Sheffer and colleagues (2018) tested sitting members of parliament in Belgium, Canada and Israel using decision-making scenarios with financial incentives. Politicians showed many of the same biases as ordinary citizens, including a willingness to take greater risks to avoid losses and a tendency to continue investing in failing projects because resources had already been committed. In one scenario, 83.7% of MPs supported extending a failing loan, compared with 71.1% of citizens (pp. 308–311).
Huber, Hill and Lenz (2012) demonstrated another form of bias: giving disproportionate importance to the most recent information. In their experiment, participants effectively valued a token earned in the final four rounds at about 1.8 times a token earned earlier, even though they had encountered the earlier information only moments before (p. 730).
The finding by Poulsen and Aisbett discussed in Essay 1 fits this pattern. Governments adjusted their behaviour after experiencing a lawsuit themselves, but barely responded to equivalent lawsuits against other governments. Direct experience received much more attention than equally relevant information about others.
Taken together, these studies suggest that political actors, including experienced decision-makers, do not process all available information equally. Their judgments depend on what they remember, what they have personally experienced, what information is most noticeable, and whether the shortcuts they use are appropriate for the situation.
Agent Attributes
To represent these processes in an ABM, agents need a small number of clearly defined attributes. Each attribute should affect behaviour in a specified way rather than simply give the agent a psychological label.
- Memory of recent events. Each agent remembers a limited number of events, including when they happened, how serious they were, and whether the agent experienced them directly or heard about them from someone else.
- Memory decay. Older events gradually have less influence on the agent’s judgment. The rate of decline is controlled by a decay parameter.
- Salience. Some events have a stronger influence than others. Particularly intense incidents and events experienced personally receive more weight.
- Perceived risk. Each agent maintains an estimate of how dangerous the environment is. This estimate is based on remembered events rather than directly on the true level of risk.
- Sophistication. Agents differ in how well they understand when a particular shortcut is useful. More sophisticated agents are generally better at applying heuristics in the appropriate circumstances.
- Information quality. Agents can remember and act on inaccurate information. Some may also be highly confident in information that is wrong.
Together, these attributes allow agents to respond differently to the same objective circumstances.
Decision Rule
The basic rule is straightforward: an agent estimates risk from the events it remembers, giving more importance to recent, intense and personally experienced events.
Consider a situation in which the actual level of violence remains unchanged. A particularly dramatic incident occurs, receives attention and becomes widely discussed. Agents who remember that incident may conclude that the environment has become much more dangerous, even though the underlying rate of violence has not changed.
Their perception of risk then gradually decreases as the incident becomes less recent. Depending on what other information they receive, perceived risk may eventually fall below the actual level of risk.
We can describe this process with two equations.
Equation 1: Perceived Risk
The equation simply calculates a weighted average of the events an agent remembers. Events with more weight have a greater influence on the result.
Here, R(t) is the agent’s perceived risk at time t, M_(t) is the set of events currently in memory, Ie is the intensity of each event, and w_e(t) is the weight assigned to it.
Equation 2: Weight of a Remembered Event
This equation determines how influential each event is. The term t-t_e represents how long ago the event occurred. The parameter lambda controls how quickly its influence fades. The term s_e represents its salience, including whether it was experienced directly or reported by someone else.
For example, we can distinguish between two salience values:
with:
This means that an incident the agent experienced personally has a much greater influence than an equivalent incident reported by a neighbour. The distinction reflects the findings of Poulsen and Aisbett (2013).
The agent then uses its perceived risk to choose an action, following the threshold-based decision rule introduced in Model Card 1. Depending on how risk is used in that model, crossing a threshold may change the agent’s willingness to protest, repress, cooperate or escalate.
Finally, the model must account for differences in sophistication. Following Lau and Redlawsk (2001), we can represent the probability that an agent applies its heuristic outside the circumstances where it works as:
This is a simplified modelling assumption, not a probability estimated directly from their study. It makes more sophisticated agents less likely to rely on an inappropriate shortcut.
Importantly, giving an agent more time to think does not automatically correct a poorly chosen heuristic. This reflects the finding by Ballew and Todorov that additional deliberation did not improve judgments based on initial facial impressions.
Interaction and Environment
So far, the model describes how individual agents form judgments based on their own memories. But in a political or security environment, people also learn about events through others.
Agents therefore exchange information through their social connections. When one agent experiences an incident, that event can enter the memories of neighbouring agents, even if they were not directly affected.
This creates a mechanism through which a local incident can influence risk perceptions across a much wider network. Perceived danger may spread faster than any actual change in the underlying threat.
Two features deserve particular attention.
First, information received from others should normally have less influence than direct experience. Reported incidents still matter, but they receive a lower weight. This allows the model to reproduce the difference between responding to something that happened to oneself and responding to an otherwise similar event affecting someone else.
Second, the information environment may be deliberately manipulated. Lupia (1994) found that actors in the California insurance-reform campaign, including the insurance industry and trial lawyers, tried to conceal their sponsorship of proposals. This made it harder for voters to use the identity of a proposal’s supporters as a shortcut (pp. 66–67).
In a security setting, a similar mechanism could occur when political actors deliberately hide, distort or manipulate the signals on which others rely.
An ABM should therefore allow not only for inaccurate judgments but also for strategic attempts to undermine the quality of the information available to other agents.
Possible ABM Implementation
A practical starting point would be to extend the protest, repress, cooperate and escalate model from Essay 1 in NetLogo.
Rather than assuming that each agent responds to a fixed or objectively measured level of danger, the model would give agents a memory-based estimate of risk. This estimate would change as events occur, are remembered and are communicated through the network.
Epstein’s (2002) civil violence model, discussed in Piece 2, offers a suitable foundation. In that model, an agent’s willingness to act depends partly on grievance and partly on the perceived probability of arrest. The risk perception component provides a natural place to introduce the memory-based heuristic.
An initial experiment could be deliberately simple.
Keep the actual rate of violence constant throughout the simulation. Then introduce a single, unusually intense incident and observe how agents respond.
Compare two populations. In the first, agents base their decisions on the actual level of risk. In the second, agents use the availability-weighted memory rule developed above.
The central question is whether a temporary, highly visible incident produces lasting changes in behaviour even though the underlying security environment has not changed.
Further experiments could vary how fast memories fade, how strongly agents respond to direct experience, how information spreads through networks, and how well different agents apply their heuristics.
Observable Emergent Outcomes
The model is designed to investigate three possible patterns.
- Overreaction followed by complacency
After a dramatic incident, perceived risk may rise much faster than actual risk. Agents may respond by supporting more aggressive policies, increasing repression or otherwise changing their behaviour. As the incident fades from memory, perceived risk declines. Under some conditions, it may fall below the true risk level, encouraging agents to relax precautions or reverse earlier decisions. The result could be a cycle of excessive response followed by insufficient preparation, even when the underlying threat remains stable. - Network-wide changes in risk perception following a local incident
An incident affecting only a small number of agents may influence perceptions throughout the network as people share information. The effect should be strongest where agents are closely connected and give substantial weight to reports received from others. The model could therefore show how public concern or political pressure becomes widespread without a comparable increase in actual danger. - Different outcomes for sophisticated and less sophisticated agents
Agents who understand when a shortcut is useful may benefit from it. Those who apply the same shortcut in inappropriate circumstances may make worse decisions. If the model includes both groups, expanding access to simple informational cues could improve decision quality for some agents while worsening it for others. This is a testable implication of Lau and Redlawsk’s findings: making information easier to access does not necessarily improve decisions equally across a population.
Limits
The main risk in this model is adding psychological concepts without giving them a clear function.
An agent described as having “availability bias” is not necessarily more realistic than an ordinary agent. The model only gains explanatory value if the bias is represented by a specific decision rule whose effects can be examined.
In this case, that means defining how quickly memories fade, how much additional weight intense or directly experienced events receive, and how these factors change decisions.
Without those specifications, the model risks using psychological terminology to describe what is effectively a simple mathematical average of past observations. This is the concern behind Gigerenzer’s warning that naming a heuristic is not the same as modelling it.
There are three further limitations.
First, heuristics do not affect everyone equally. A shortcut that improves one agent’s decisions may worsen another’s. Applying a single bias parameter to an entire population would therefore be difficult to justify.
Second, much of the evidence comes from research on voters and elected politicians. The mechanisms may also apply to intelligence analysts, military planners or security officials, but the studies discussed here do not establish that directly.
Third, the numerical findings in these studies come from particular populations and experimental settings. They provide a basis for choosing which mechanisms to include in the model, but they do not tell us exactly how quickly memories should decay or how much more weight an agent should give to direct experience.
Those parameter values would need to be estimated or tested against the specific political or security environment being modelled.
Model Idea
Agents judge risk based on recent, vivid and personally experienced events rather than relying entirely on the underlying level of danger. This can make them overreact to highly visible incidents while underestimating risks that develop gradually or are known mainly through second-hand reports. The accuracy of their judgments depends on how well their shortcuts fit the environment, and agents differ in their ability to make that assessment.
Further Reading
Ballew, C. C., II, & Todorov, A. (2007). Predicting political elections from rapid and unreflective face judgments. Proceedings of the National Academy of Sciences, 104(46), 17948–17953.
Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3(1), 20–29.
Huber, G. A., Hill, S. J., & Lenz, G. S. (2012). Sources of bias in retrospective decision making: Experimental evidence on voters’ limitations in controlling incumbents. American Political Science Review, 106(4), 720–741.
Kuklinski, J. H., Quirk, P. J., Jerit, J., Schwieder, D., & Rich, R. F. (2000). Misinformation and the currency of democratic citizenship. Journal of Politics, 62(3), 790–816.
Lau, R. R., & Redlawsk, D. P. (2001). Advantages and disadvantages of cognitive heuristics in political decision making. American Journal of Political Science, 45(4), 951–971.
Lupia, A. (1994). Shortcuts versus encyclopedias: Information and voting behavior in California insurance reform elections. American Political Science Review, 88(1), 63–76.
Sheffer, L., Loewen, P. J., Soroka, S., Walgrave, S., & Sheafer, T. (2018). Nonrepresentative representatives: An experimental study of the decision making of elected politicians. American Political Science Review, 112(2), 302–321.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
The next piece in the series moves from individual decision rules to the modelling method itself: how to translate behavioural theories into specific, testable agent rules without introducing unsupported assumptions. That is Essay 8, From Behavioural Theory to ABM Design.