The amplification of risk in experimental diffusion chains

Abstract

Understanding how people form and revise their perception of risk is central to designing efficient risk communication methods, eliciting risk awareness, and avoiding unnecessary anxiety among the public. However, public responses to hazardous events such as climate change, contagious outbreaks, and terrorist threats are complex and difficult-to-anticipate phenomena. Although many psychological factors influencing risk perception have been identified in the past, it remains unclear how perceptions of risk change when propagated from one person to another and what impact the repeated social transmission of perceived risk has at the population scale. Here, we study the social dynamics of risk perception by analyzing how messages detailing the benefits and harms of a controversial antibacterial agent undergo change when passed from one person to the next in 10-subject experimental diffusion chains. Our analyses show that when messages are propagated through the diffusion chains, they tend to become shorter, gradually inaccurate, and increasingly dissimilar between chains. In contrast, the perception of risk is propagated with higher fidelity due to participants manipulating messages to fit their preconceptions, thereby influencing the judgments of subsequent participants. Computer simulations implementing this simple influence mechanism show that small judgment biases tend to become more extreme, even when the injected message contradicts preconceived risk judgments. Our results provide quantitative insights into the social amplification of risk perception, and can help policy makers better anticipate and manage the public response to emerging threats.

Keywords: collective behavior; diffusion chains; opinion dynamics; risk perception; social transmission.

Fig. 1.

Topological map of information propagation in an experimental diffusion chain. Among all units of information available at chain position 1 (blue dots), only three have survived to the end of the chain, although they were strongly distorted. The text on the right-hand side describes the categories of these units of information. Seven units of information were introduced by the chain (squares), two of which survived to the end of the chain. The color coding indicates the cumulated content distortion of the information. Information identifications (y axis) are arbitrary.

Fig. 2.

Dynamics of information propagation. (A) Mean number of units of information transmitted and the mean cumulated content distortion of the information over the chains. (B) Hazard functions showing the probabilities that a piece of information disappears (red, pDeath), gets distorted (blue, pDistortion), or appears (gray, pBirth) at each chain position. (C) Distribution of the message differentiation D_ij for all possible pairs of messages i and j at position 1 (red line), position 3 (blue line), and position 10 (gray line). The distributions tend to shift toward high differentiation values, indicating that the content of the message becomes increasingly different between the chains as it propagates from one person to another. (Inset) Mean value of D_ij at each position of the chain (gray dots). The dark line corresponds to the fit equation $f\left(x\right)=p_{1}x/(x+p_2)$ with parameters p₁ = 0.86 and p₂ = 0.61.

Fig. 3.

Message mutation. (A) Evolution of the normalized number of positive statements $n_p^{+}$ (blue), negative statements $n_p^{-}$ (red), and total statements $n_p$ (gray) over the chain. Fit lines are power functions $f\left(x\right)=x^{-e}$, where the exponent e equals 0.96, 0.62, and 0.57 for $n_p^{+}$, $n_p^{-}$, and $n_p$, respectively. (Inset) Average proportion of positive statements (${\omega }_p^{+}$, in blue) and negative statements (${\omega }_p^{-}$, in red) along the chains. (B) Distribution of the filtering coefficients $k_p^{+}$ and $k_p^{-}$ for all subjects. The distribution of $k_p^{-}$ is significantly higher than the distribution of $k_p^{+}$ (P < 0.001). (C) Individual profiles measured as the pair {$k_p^{+}$,$k_p^{-}$}. Each point represents one experimental subject. Individuals with $k_p^{+}\approx k_p^{-}$ have a neutral effect on the message, whereas individuals with $k_p^{-}$ > $k_p^{+}$ (respectively $k_p^{+}$ > $k_p^{-}$) tend to make the message more alarming (respectively more reassuring).

Fig. 4.

Social influence. The scatter plot represents the degree to which participants changed their risk perception as a function of the initial deviation with the message signal they have received. Subjects receiving a message with negative signal, with respect to their initial perception, tend to increase their risk level, whereas those subjects receiving a more positive signal tend to reduce their risk level. The equation of the fit line is $y=0.45x-0.14$.

Fig. 5.

Computer simulations. (A) Evolution of the risk signal at chain positions 1, 10, and 50 as a function of the initial risk perception of the individuals (x axes) and the signal of the injected message (y axes). The gradual dominance of extreme values (in dark red and dark blue) demonstrates the amplification of the risk signal. (B) Evolution of individuals’ risk perception follows a similar trend. Individuals who initially expressed extreme risk perception (x = 0 or x = 1) gradually move back to their initial view, regardless of the message signal. The social influence parameter is set to s = 0.5. Simulations varying the value of s are provided in Fig. S4. Results are averaged over 500 simulations.