A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries

Sustainable management of complex human–environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed by the past experience, must not be unduly constrained; they must allow equally for both the fine-tuning of successful existing approaches and the generation of novel ones in unbiased ways. We study ocean fisheries as an example class of complex human–environmental systems, and present a new model (POSEIDON) and computational approach to policy design. The model includes an adaptive agent-based representation of a fishing fleet, coupled to a simplified ocean ecology model. The agents (fishing boats) do not have programmed responses based on empirical data, but respond adaptively, as a group, to their environment (including policy constraints). This conceptual model captures qualitatively a wide range of empirically observed fleet behaviour, in response to a broad set of policies. Within this framework, we define policy objectives (of arbitrary complexity) and use Bayesian optimization over multiple model runs to find policy parameters that best meet the goals. The trade-offs inherent in this approach are explored explicitly. Taking this further, optimization is used to generate novel hybrid policies. We illustrate this approach using simulated examples, in which policy prescriptions generated by our computational methods are counterintuitive and thus unlikely to be identified by conventional frameworks

On the quest for defining organisational plasticity: a community modelling experiment

Purpose: This viewpoint article is concerned with an attempt to advance organisational plasticity (OP) modelling concepts by using a novel community modelling framework (PhiloLab) from the social simulation community to drive the process of idea generation. In addition, the authors want to feed back their experience with PhiloLab as they believe that this way of idea generation could also be of interest to the wider evidence-based human resource management (EBHRM) community. Design/methodology/approach: The authors used some workshop sessions to brainstorm new conceptual ideas in a structured and efficient way with a multidisciplinary group of 14 (mainly academic) participants using PhiloLab. This is a tool from the social simulation community, which stimulates and formally supports discussions about philosophical questions of future societal models by means of developing conceptual agent-based simulation models. This was followed by an analysis of the qualitative data gathered during the PhiloLab sessions, feeding into the definition of a set of primary axioms of a plastic organisation. Findings: The PhiloLab experiment helped with defining a set of primary axioms of a plastic organisation, which are presented in this viewpoint article. The results indicated that the problem was rather complex, but it also showed good potential for an agent-based simulation model to tackle some of the key issues related to OP. The experiment also showed that PhiloLab was very useful in terms of knowledge and idea gathering. Originality/value: Through information gathering and open debates on how to create an agent-based simulation model of a plastic organisation, the authors could identify some of the characteristics of OP and start structuring some of the parameters for a computational simulation. With the outcome of the PhiloLab experiment, the authors are paving the way towards future exploratory computational simulation studies of OP

Voter reasoning bias when evaluating statements from female and male political candidates

The Women and Politics Research Section of the American Political Science Association 2018. The article examines whether female political candidates are disfavored in terms of persuasiveness potential based on their expertise and trustworthiness. Using a Bayesian argumentation paradigm in which candidates endorse policies, this study shows that male voters regard female candidates as less persuasive than male candidates. A controlled between-subjects experiment among 202 potential voters in the United States suggests that female election candidates are subject to sex biases in two central ways. First, despite agreeing on their trustworthiness and expertise, male voters find highly credible female candidates less persuasive than identical male candidates. Second, female candidates are affected more adversely if they are perceived as lacking in trustworthiness. Male candidates, on the other hand, are affected more negatively if they are perceived as lacking in expertise. Whereas perceived lack of expertise is relatively easy to repair, trustworthiness may be difficult to regain once it is lost. In a political environment in which attack ads are prevalent, this may carry a greater negative impact for female candidates

Competing causal debates and the influence of source credibility on belief revision

This research explores the effects of the zero-sum fallacy and the interaction of source credibility in three experimental parts (Pilditch, Fenton & Lagnado, 2019). The zero-sum fallacy is a reasoning error wherein individuals presented with two equally plausible competing causal debates erroneously assume that neither can be true. Experiment 1 (N=16) was an unsuccessful replication of Pilditch and colleagues (2019) experiment 1 which previously significantly demonstrated the effects of the zero-sum fallacy. Experiment 2 (N=53), found significant results favouring the existence and robustness of the zero-sum fallacy using logically identical but contextually different experimental stimuli. Experiment 3 (N=101), found the zero-sum fallacy persisted when source credibility statements were incorporated, but that source credibility had a significant impact on participants’ reasoning. As part of explanatory analysis, data from all 3 experiments was subjected to Bayesian analysis

Comparing predictions from the Elaboration Likelihood Model and a Bayesian model of argumentation

Much of our knowledge comes from other people. In considering how argument quality and source reliability influences message persuasiveness, we conduct a comparison of the Elaboration Likelihood Model of Persuasion and the Bayesian Model of Argumentation, which are based on different assumptions. Participants were asked to judge a fictitious character’s degree of belief in a claim given evidence. To test competing predictions, we manipulate the character’s elaboration level, the argument’s quality, and the source’s reliability. The elaboration did not moderate the main effects of argument quality and source reliability, as they both were integral to the overall message strength in both high and low elaboration conditions. Bayesian predictions have better fit with the observed data, whilst ELM predictions did not align well. Overall, the BM is supported, but we discuss how this model could be further improved while the ELM is contested