The Roots of Decision-Making Bias

Cognitive Biases

At its core, behavioral economics examines systematic errors in judgment that people make – known as cognitive biases. These aren't random mistakes; they’re predictable patterns of deviation from rationality.

Examples include the *availability heuristic*, where we overestimate the likelihood of events readily available in our memory (e.g., fearing plane crashes more than car accidents), and the *confirmation bias*, leading us to seek out information that confirms pre-existing beliefs.

Prospect Theory

Developed by Daniel Kahneman and Amos Tversky, Prospect Theory revolutionized our understanding of risk aversion. It posits that people don't evaluate potential gains and losses equally.

Instead, we are more sensitive to losses than equivalent gains – a phenomenon known as loss aversion. This impacts investment decisions and risk-taking behavior.

Loss Aversion = (Sensitivity to Loss) > (Sensitivity to Gain)

Framing Effects

The way information is presented – or ‘framed’ – can significantly alter our choices, even if the underlying objective remains the same.

For example, a medical procedure described as having an 80% survival rate is perceived more favorably than one with a 20% mortality rate, despite representing identical outcomes.

Applications in Simulation

Within this simulator, we'll observe how incorporating behavioral biases – such as loss aversion or overconfidence – impacts simulated market behavior.

Users can experiment with different levels of bias to see how it affects investment strategies and overall portfolio performance. This highlights the importance of understanding human psychology in financial modeling.

Frequently asked questions

Try it live

Everything above runs in your browser — open Behavioral Economics: Loss Aversion & Anchoring Bias and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.