The standard definition of “behavioral economics” is that it brought insights from psychology to bear on economic decision-making: that is, concepts like using rules-of-thumb in situations with limited information, valuing the present over the future, difficulties in perceiving probabilities accurately, habit formation, peer effects, and more. But the most recent edition of the Behavioral Economics Guide 2026, edited by Alain Samson, includes two essays suggesting that it’s time for additional steps. In a similar spirit, Ulrike Malmendier argues that it’s time to take personal experience effects into account; Isabelle Brocas argues for applying insights from the biological sciences.
In “Homo Experiens: Why Behavioral Economics Needs the Life Sciences,” Malmendier points to a body of research which finds (perhaps unsurprisingly to anyone but economists) that past experience influences behavior. For example, the stock-market returns that people have experienced during their lives have a strong effect on people’s likelihood of investing in the stock market. Those who have lived through periods of higher inflation are more likely to expect higher rates of future inflation. Even experts, well-acquainted and up-to-date with evidence, are not immune to these experience effects. In a study of members of the Federal Reserve Board of Governors, those who had lived through periods of higher inflation were less likely to favor lower interest rates in the future. The beliefs of fund managers and doctors are shaped by life experiences.
Malmendier suggests that economic decision-making should expand to consider homo experiens, as she puts it. She writes:
Yet, the behavioral economics revolution has stalled at a decisive point. The human element I appealed to above is still missing, or at least incomplete. To put it in stark terms, human behavior still seems rather robotic. Where previously, in the neoclassical model, it was the output of a perfectly programmed computer, in the behavioral economics model it became the output of a not-quite-so-perfect computer program, one prone to systematic bugs. And while the discovery of heuristics and biases was a genuine improvement, the resulting models remained mechanical. Once we identify the biases and model them, the program is assumed to run the same way for everyone, at every point in their lives. What you have lived through, what you have felt, what has happened to your body and brain along the way—none of that enters the model.
In practice, how might such a research agenda be carried out? A starting point is just to take life experience into account as a variable. But Malmendier has more in mind:
The implication is that the biological channels through which experiences affect us—stress hormones, neural rewiring, immune and endocrine responses—are real, measurable, and consequential for the economic outcomes we care about. And they are almost entirely absent from economic models, whether behavioral or not. … I believe that the next chapter of behavioral economics lies in a turn towards the life sciences—in taking seriously what neuroscience, psychiatry, medicine, and biology already know about human beings and how they are shaped by what they live through. This means, first, that economists should start collecting data on stress, emotions, hormones, and physical health alongside the economic variables we traditionally measure. So far, economists have rarely gathered data along these dimensions, and the standard sources of survey data—the ACS [American Community Survey], the Michigan Survey of Consumers, the Survey of Consumer Finances—contain few variables that are related. …
In another essay in the same volume, Isabelle Brocas takes a related by different angle in “Beyond Preferences: The Biological Foundations.” Her focus is that behavioral economics can often seem like a list of possible biases and heuristics, operating with a various domains of decision-making, but with little sense that the domains are interrelated. She offers a useful diagram. The peach-colored labels around the outside focus on areas often studied by behavioral economists. The green labels show the brain mechanisms behind these areas of decision-making. As the graph usefully illustrates, mechanims that affect one domain are likely to affect several domains.
Perceiving this system as a unified whole, rather than a one-off list of individualized bits and pieces, seems like a worthy goal. Brocas writes:
One of the [economics] discipline’s great strengths has been its ability to isolate dimensions of behavior. Risk preferences, intertemporal preferences, ambiguity attitudes, social preferences, strategic sophistication, and self-control have all generated productive literatures. Yet, this partitioning has also come at a cost. It has encouraged the idea that these domains correspond to separable psychological objects, when many may reflect overlapping underlying mechanisms.
Take risk and time. Economists often treat them as distinct dimensions of preference: one concerns uncertainty, the other delay. But that separation becomes less convincing once we think mechanistically. Both types of decisions recruit valuation, affective anticipation, future representation, attention to salient outcomes, learning from feedback, and inhibitory control. A person who is impulsive may also appear more willing to take risks, not because impatience and risk-loving are identical constructs but because both may be shaped by common patterns of reward sensitivity, limited future simulation, stress, or weaker self-regulation. Similar overlaps likely exist between strategic reasoning and working memory, between cooperation and emotional regulation, between trust and threat perception, and between consumer choice and attentional bottlenecks. The traditional language of preferences can therefore obscure as much as it reveals. It describes regularities at the level of outcomes, but it does not tell us whether those regularities arise from common mechanisms or genuinely distinct systems.
Brocas argues that fruitful work can be done by looking at biology, and perhaps especially work in neuroeconomics.
A biologically informed economics does not require economists to abandon preferences or reduce behavior to neurons and genes. Preferences remain useful reduced-form representations when behavior is stable and prediction is the objective. The point is different: when economists seek to explain heterogeneity, diagnose why interventions fail, or design policies that work across contexts and populations, it is often necessary to model the mechanisms that generate the outward pattern of choice. Biology can enter economics not only empirically, but also theoretically through models that derive observed behavior from underlying brain processes and resource constraints. This perspective clarifies what is malleable, when it is malleable, for whom, and through which policy levers. Taking biology seriously is therefore not a detour from economic theory—it is a step toward a more explanatory and more useful science of human behavior. …
Neuroeconomic work has been especially valuable here because it shows that value-based choice relies on a sequence of interacting computations rather than on isolated behavioral modules. The act of choosing involves representing the problem, assigning subjective values, selecting an action, experiencing an outcome, and updating behavior through learning (Rangel et al., 2008). This logic already pushes against the idea that risk, time, social choice, and self-control should be understood as self-contained domains.
I can barely keep up with the broad field of economics as it is. I frankly despair of becoming knowledgable in the neuroeconomics of brain-based decision-making, including “stress hormones, neural rewiring, immune and endocrine responses.” But I’m always intrugued by the work of economists like Malmendier and Brocas who are willing and able to make the effort.
