Regret, Uncertainty, and Bounded Rationality in Norm-Driven Decisions

TL;DR

The paper develops a regret–uncertainty framework within an agent-based, norm-driven vaccination model to explain preventive behavior during epidemics. It couples a SIR epidemic on a multiplex network with boundedly rational agents whose vaccination choices arise from material payoffs, anticipated regret, and three types of social norms. The results show non-linear effects: intermediate rationality yields best coordination, moderate regret and uncertainty promote caution and adaptive behavior, and social norms compensate for incomplete information, with personal norms and injunctive/descriptive cues playing distinct roles. The findings offer a psychologically grounded, computationally explicit account of how emotion, cognition, and social influence shape vaccination uptake and epidemic outcomes, with implications for designing normative interventions.

Abstract

This study introduces an agent-based model to study how regret, uncertainty, and social norms interact to shape vaccination behavior during epidemics. The model integrates three behavioral mechanisms, anticipated regret, evolving norms, and uncertainty-dependent trust, within a unified learning framework. Grounded in psychology and behavioral economics, it captures how individuals make probabilistic choices influenced by material payoffs, fear, trust, and social approval. Simulations of the Susceptible-Infected-Recovered process show that collective outcomes are best when agents display an intermediate level of rationality; they deliberate enough to respond to risk but remain flexible enough to adapt, avoiding the instability of both random and overly rigid decision-making. Regret exerts a dual influence; moderate levels encourage adaptive self-correction, while excessive regret or greed destabilize choices. Uncertainty has a similarly non-linear effect; moderate ambiguity promotes caution, but too much uncertainty disrupts coordination. Social norms restore cooperation by compensating for incomplete information. Personal norms guide behavior when individuals have reliable information and feel confident in their judgments. Injunctive norms-signals of others' approval-become more influential under uncertainty, while descriptive norms, which arise from observing others' actions, provide informational cues that help people decide what to do when direct knowledge is limited. Overall, the model provides a psychologically grounded, computationally explicit account of how emotion, cognition, and social norms govern preventive behavior during epidemics.

Figures (7)

• Figure 1: Evolutionary Update Rule
• Figure 2: (A): Schematic of the decision-making process. The diagram illustrates how learning and social norms jointly determine each agent’s vaccination intention, weighted by their relative influence. Two assumptions guide this interaction: (a) The less safe an agent feels—i.e., the lower $S_{i}(t)$, representing infection risk from the previous season—the more they rely on empirical cues (learning or empirical expectations) rather than normative ones (personal beliefs or injunctive expectations). Similarly, the more uncertain the agent feels about the knowledge he posses, the more he will consult his injunctive, moral variables (personal and normative expectations). (b) The more heterogeneous and unstable the environment—when agents’ recent decisions diverge from one another or from past averages—the greater the reliance on personal experience and beliefs instead of social imitation. (B): Schematic of the dynamics of social norm variables. The diagram shows how different factors shape norm evolution under two key assumptions: (a) Each agent updates her norms with probability $\gamma_{i}$ under external influence, otherwise combining personal beliefs, existing norms, and peers’ actions. (b) Environmental instability—large deviations between recent and past behaviors and low consensus among neighbors—increases dependence on personal factors over conformity to others.
• Figure 3: Behavioral and epidemiological effects of memory and social norms.(A) Fraction of infected individuals vs. infectivity rate $\beta$. Without social norms, increasing $\beta$ raises infection levels. Comparing memory lengths $m=1$ (blue), $m=2$ (green), and $m=4$ (black), longer memory—hence greater informational depth—consistently lowers infections. When agents also consider perceived norms (red), infections drop further for the same memory, indicating that normative feedback stabilizes preventive behavior. (B) Fraction of vaccinated individuals vs. infectivity rate $\beta$. Vaccination coverage rises with both $\beta$ and memory but quickly saturates. Including social norms ($m=4$) does not increase mean vaccination relative to learning-only agents yet improves its spatial distribution. Norms therefore enhance coordination and reduce clustering rather than overall uptake ($k_{\mathrm{rat}}=0.1$). (C) Evolution of social norms vs. infectivity rate $\beta$. The intention $\Delta U_i$ and the three norm components remain misaligned because they adapt at different rates: empirical expectations adjust fastest, while personal and injunctive norms evolve more slowly ($k_{\mathrm{rat}}=0.1$). (D) Fraction of infected individuals vs. memory length. With only learning (OL), infections decline sharply as memory increases, reflecting improved decision accuracy. When social norms (SN) are included, infection rates become less sensitive to memory, showing that normative feedback substitutes for experience. Social influence thus reduces reliance on individual memory to sustain cooperative vaccination.
• Figure 4: Fraction of infected individuals as a function of rationality. When agents rely only on learning (OL), infection levels show a clear minimum at intermediate rationality, reflecting an optimal trade-off between exploration and exploitation. Highly irrational agents (large $k_{\mathrm{rat}}$) fail to process information effectively, sustaining high infection rates, while highly rational agents (small $k_{\mathrm{rat}}$) explore too little and oscillate between free-riding and full compliance. Including social norms (SN) removes these oscillations, as normative feedback stabilizes decisions and generally reduces infections. Yet, for very irrational agents, excessive dependence on social cues can backfire—individual learning alone may perform better.
• Figure 5: Behavioral impact of regret and greediness parameters.(A) Fraction of infected individuals vs. regret strength $\eta_{1}$. With greediness fixed at $\eta_{2}=1.0$, rational agents ($k_{\mathrm{rat}}=0.1$) perform worse as regret increases without norms, while outcomes remain stable when norms are included. For less rational agents ($k_{\mathrm{rat}}=0.5$), higher $\eta_{1}$ improves results in both cases: regret fosters adaptive exploration among boundedly rational individuals but triggers emotional overreaction in highly rational ones. (B) Fraction of infected individuals vs. greediness $\eta_{2}$. At fixed regret strength $\eta_{1}=1.0$, reducing greediness benefits rational agents in both norm and no-norm scenarios, with larger gains under normative influence. For less rational agents, the effect is non-monotonic: extreme greed (low $\eta_{2}$) or indifference (high $\eta_{2}$) both worsen outcomes, whereas intermediate values yield lower infection levels. Moderate greed thus enhances exploration and learning stability. (C) Fraction of infected individuals vs. regret strength $\eta_{1}$ for $k_{\mathrm{rat}}=0.1$. (D) Same for $k_{\mathrm{rat}}=0.5$. For rational, greedy agents ($k_{\mathrm{rat}}=0.1$, $\eta_{2}=0.1$), increasing $\eta_{1}$ erases the distinction between norm and no-norm conditions, indicating that individuals cease to learn from peers. For less rational agents ($k_{\mathrm{rat}}=0.5$), an intermediate $\eta_{1}$ minimizes infections, showing that moderate regret promotes behavioral correction and coordination.