Ensuring Effective Public Health Communication: Insights and Modeling Efforts From Theories of Behavioral Economics, Heuristics, and Behavioral Analysis for Decision Making Under Risk

Abstract

Public health (PH) messaging can have an enormous impact on shaping how individuals within society behave, and can ensure it is in a safe and responsible way, consistent with up-to-date evidence-based PH guidelines. If done effectively, messaging can save lives and improve the health of those within society. However, unfortunately, those within Government PH bodies typically have little training about how to effectively represent PH messages in a way that is consistent with psychological theories of cognitive bias, in order to avoid cognitively biasing the public through their messages. As a result of this, inadequate representation of PH messages can result, which can often lead to cognitive bias in those from the public who read or listen to the message information. This can lead to poor decision making of the pubic as a whole, which can then further lead to harm and even death of public members as a result of these poor decisions. One way to minimize the problem of bias in decision making is to explore psychology theories that model how bias can occur from PH messaging, and identify ways in which PH agencies can utilize such approaches to improve the effectiveness of their messages. Previous focus has been largely on behavioral economic theories, however, here, other accounts are offered in addition to these. These include theories of heuristics and theories from the behavior analysis domain, which may increase the predictive power of modeling bias, and have applications for how best to represent PH message information which minimize bias.

Going beyond the DSM in predicting, diagnosing, and treating autism spectrum disorder with covarying alexithymia and OCD: A structural equation model and process-based predictive coding account

“…This approach is more relevant at the ideographic level as individuals each form their own unique relational networks through their unique learning histories. It could also be extended even further with an RFT implemented reinforcement machine learning interpretation through discrete time series Markov chains (Edwards, 2021 ) to analyze reinforced behavior (and any other EMA data) through time in a complex distributed system (i.e., the real-world environment, and outside of the laboratory) (Dabrowski and Hunt, 2011 ).…”

Prospect Theory: A Bibliometric and Systematic Review in the Categories of Psychology in Web of Science

“…They are risk-seeking when confronted with information about losses, but risk-averse when confronted with information about gains [ 19 ]. Thus, in the health field, gain-frames may be more beneficial to promote preventive behaviors, as well as loss-frames to favor detection behaviors [ 24 ]. One possible explanation is that prevention behaviors are perceived as low risk, while detection behaviors are perceived as high risk [ 19 , 25 , 26 ].…”

A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge

“…However, equation 4 can be expressed more succinctly in the form of ME if the symbol | | | is used to denote a shared relation (AND) within the set as suggested in a previous studies ( Gilroy, 2015 ; Edwards, 2021 ). In the following example of ME, describing a five stripe snake (A) as being “more dangerous” (R x ) than a three stripe snake (B) derives the relation through ME that, therefore, a three stripe snake (B) must be “less dangerous” (R y ) than a five stripe snake (A), whereby a contextual relation is expressed by C rel within the set.…”

“…This approach has the advantage of not needing to specify which categories are stored within background knowledge hence gets around the knowledge selection problem, instead the semantic network derived a function between the input and output properties through its distributed activations across layers. Further to this, it is important to note that this model could be expanded further (as an additional module) to include a reinforcement learning agent structured through a Markov decision process (MDP) as specified in previous work (Edwards, 2021), when more complex decision making is needed which requires the extracting of background knowledge for category decision making. This specifies the probability P given some action a, and is denoted as P a s, s 1 , s is the current state of 1 See (Edwards, 2021) for full details of this extended reinforcement framework.…”

“…Further to this, it is important to note that this model could be expanded further (as an additional module) to include a reinforcement learning agent structured through a Markov decision process (MDP) as specified in previous work (Edwards, 2021), when more complex decision making is needed which requires the extracting of background knowledge for category decision making. This specifies the probability P given some action a, and is denoted as P a s, s 1 , s is the current state of 1 See (Edwards, 2021) for full details of this extended reinforcement framework. the environment and s is some new state transition if action a is carried out.…”