Coming and Going in Loops: Participatory Modelling of a System with All its Complexity

Social marketing is currently involved in pursuing several important theoretical and methodological goals pertaining to wide-scale behavior change. The lack of complex system understanding via highly participatory processes and feedback loops is a major impediment for systemic behavior change. The purpose of this paper is to show how the implementation of participatory modelling to explore networks of feedback loops can empower social marketing in capturing system complexity. As a case study, a group of system stakeholders qualitatively modelled a cycling system in a city setting to uncover the system's core behavioral dynamics. This participatory modelling process revealed that the interactions within and between three feedback loops were mainly responsible for the cycling system issues. These feedback loops were: (a) output-based and autocratic decision-making, (b) an abundance of conflicted interests and (c) the reinforcement of a car-dominant paradigm in people's minds. The paper contributes to understanding the potential of participatory modelling for multi-level behavior change.

Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.

Fine-scale behavior of red snapper (Lutjanus campechanus) around bait: approach distances, bait plume dynamics, and effective fishing area

The behavior of fish around bait is poorly understood despite it being important for the fish catching process and estimating relative abundance. We used a fine-scale acoustic positioning system to quantify the movements of 26 red snapper (Lutjanus campechanus) around 120 bait deployments in 2019 at a natural reef site (~37-m deep) in North Carolina, USA. There were 39 instances of tagged red snapper approaching bait during four baiting days, some of which approached due to apparent sensory cues (28%) while most approached incidentally (72%). Tagged red snapper approached bait from initial distances of 1 to 1 147 m (median = 27 m; mean = 86 m), and took 0 – 77 min (mean = 22 min) to approach. Fish were more likely to approach bait if they were located close to, and down-current of, the bait at deployment. Our estimated effective fishing area of 2 290 m2 (within which >50% of red snapper responded to bait) could be used along with video counts and other information to estimate densities of red snapper.

Ambivalence and Adherence to Measures to Reduce the Spread of COVID-19

The COVID-19 pandemic poses one of the largest behavioral change challenges in the last decades. Because currently, there is no widely available pharmaceutical treatment available to contain the spread of infection, governments worldwide rely – at least to some extent – on behavioral recommendations aimed at reducing spread. The success of this strategy is dependent on the number of people that follow the recommendations. Most recommendations need people to change their behavior or adopt a new behavior. We propose that such behavioral change, with direct costs and delayed benefits, is a source of conflict and mixed feelings. This ambivalence negatively affects adherence to such recommendations. We present three studies that support our hypotheses: the more ambivalent people are about the recommendations, the less they follow them. We also examined the effect of the mixed emotions of hope and worry on adherence and find that it positively relates to adherence. Our findings replicated both in a U.S. sample as well as a representative German sample. Our work is the first to investigate the role of ambivalence in large-scale behavior change and highlight the importance of understanding the conflict that comes with changing behavior. We discuss implications for policy and communication.

The Better-Than-Average Effect in the Era of COVID-19: Cognitive Bias in the Age of Pandemic and its Relationship to Vaccination Hesitancy (Preprint)

BACKGROUND The ongoing coronavirus pandemic is an unprecedented global health crisis. Because large-scale behavior change has been critical to slowing the spread of the virus, understanding the mechanisms behind people’s decisions and behaviors to follow (or not) public health recommendations, is essential. OBJECTIVE In order to investigate one possible mechanism, we investigated the presence of the better-than-average effect. METHODS in 3066 individuals across Poland, Iran, and Kazakhstan. RESULTS Participants demonstrated clear the BTAE in all three countries. Furthermore, we found that the level of BTAE was a predictor of COVID-19 vaccination (declarative) claims. CONCLUSIONS These findings contribute to the growing literature on the role of cognitive biases on health behaviors, particularly during global health emergencies. We provide recommendations for public health communicators on how to address this bias to help ensure people adopt the behaviors that are critical to combatting the virus.