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

Comparing cognitive models in dynamic agent-based models: A methodologicalcase study

Dynamic models, such as agent-based models (ABMs), are becoming an increasingly common modelling tool in cognitivesciences. They enable cognitive scientists to explore how computational, analytic models scale up when placed in complex,interactive, and dynamic environments where agents can sequentially interact over time and in space. Frequently, ABMsare built to yield a particular behaviour (riots, echo chamber emergence, etc.). As such, some models may bake in thedesired behaviour. However, many models may yield this behaviour, making it difficult to discriminate between theadequacies of each computational model. The paper directly addresses this methodological challenge. We explore a casestudy (fisheries). Agents make decisions in this dynamic and complex environment. Given a rich data set against whichto calibrate and validate model predictions, we compare and contrast statistical, adaptive, and perfect agents. We showthat adaptive computational agents equal statistical agents in calibration and outperform them for validation. In addition,we show that perfect and random agents fare poorly. This provides a method for using dynamic, agent-based models tochoose between computational model

Triggering the tragedy: the simulated effects of alternative fisher goals on marine fisheries and fisheries policy

Avoiding the ‘tragedy of the commons’ remains a challenge in many natural resource systems, and open-access fisheries are a well-studied in this context. Here, an agent-based model is used to investigate how variation in fisher goals change what policies best solve the tragedy. When fishers’ goals are easily satisfied, commons problems are avoided without management interventions but the imposition of quota limits triggers the tragedy. Thus, commons problems are not necessarily inevitable and sophisticated governance institutions or regulations are not always required to manage them; the same policy may prevent the tragedy or trigger it, depending on the fisher's goals. Given that it is difficult to ascertain them, by using a simulation model we can find patterns that help us identify fishers' goals and incorporate these patterns within our management procedure. This can assist adaptive management to better incorporate behaviour into policy evaluation

Modelling adaptive and anticipatory human decision-making in complex human-environment system

To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g. fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within populations, can lead to substantial deviations and unanticipated effects of interventions. This paper presents a computational model of fisher agents’ decision-making that draws on theoretical and empirical psychological insights to enrich this critical component. The model includes mechanisms for information integration (learning), social comparisons, and thresholds for economic satisfaction. In offering this enriched account, the model captures how fishers may adapt behaviourally given changes in policy, economic conditions, or social pressures. Furthermore, the model can be parameterised to capture the effects of different socio-cultural contexts can be simulated. The model of fisher agents has been implemented as part of POSEIDON (an agent-based fisheries management model), showing that fishers imbued with the model learn and adapt when responding dynamically to changing conditions. The model is thus demonstrated in a fisheries environment, but we discuss how its architecture could be implemented for simulation in other human-environment systems, such as designing policies to combat the human-environment problems

Impact of laparoscopy and ultrasonography in gastrointestinal malignancies.

BACKGROUND/AIMS: An adequate preoperative disease staging is highly required before surgical treatment, even in gastrointestinal malignancies. Our study wants to give a contribution in order to define echolaparoscopy weight in gastrointestinal tumors and its impact in surgical therapy. METHODOLOGY: 33 patients were affected by pancreas, 22 by stomach, 16 by extrahepatic biliary tract and 18 by liver cancers; every patient was considered worthy of radical or palliative surgery according to preoperative staging (thorax-abdominal CT and percutaneous ultrasonography). Paired sample t-tests were used to analyze the results of each methodical and probability values of less than 0.05 were considered significant. RESULTS: Preoperative instrumental examinations gave correct evaluations only in 44 of 89 cases (49%) while echolaparoscopic gave correct evaluations in 82 on 89 cases (92%) (P<0.05). So after echolaparoscopic in only 7 cases we performed an explorative laparotomy. CONCLUSIONS: Laparoscopy and ultrasound impact on therapy is worthy of attention. It seems to be able to give advantages in staging gastrointestinal malignancies, except for pancreas cancers, in which some limits and negative aspects have been demonstrated, regarding the possibility of giving correct diagnosis of portal axis infiltration

Simple Adaptive Rules Describe Fishing Behaviour Better than Perfect Rationality in the US West Coast Groundfish Fishery

Most bio-economic models in fisheries assume perfectly rational profit-maximizing behaviour by fishing vessels. Here we investigate this assumption empirically. Using a flexible agent-based model of fishing vessels called POSEIDON, we compared predicted fishing patterns to observed patterns in logbook data, that resulted from a wide range of stylized decision-making processes in the U.S. west coast dover sole-thornyhead-sablefish (DTS) fishery, which is managed with tradable quotas (ITQs). We found that observed vessel behaviour was best predicted in the model by simple decision algorithms whereby vessels chose between exploring new fishing grounds and revisiting previous ones based on their and other vessels’ past successes. In contrast, when the model assumed that vessels were perfect profit maximizers, the model substantially overestimated their profits and utilization of quota of rare, constraining species that carry high quota costs, such as yelloweye rockfish. Our results suggest that bounded rationality is an important driver of vessel behaviour in this fishery

Rejection sampling and agent-based models for data limited fisheries

Many of the world’s fisheries are “data-limited” where the information does not allow precise determination of fish stock status and limits the development of appropriate management responses. Two approaches are proposed for use in data-limited stock management strategy evaluations to guide the evaluations and to understand the sources of uncertainty: rejection sampling methods and the incorporation of more complex socio-economic dynamics into management evaluations using agent-based models. In rejection sampling (or rejection filtering) a model is simulated many times with a wide range of priors on parameters and outcomes are compared multiple filtering criteria. Those simulations that pass all the filters form an ensemble of feasible models. The ensemble can be used to look for robust management strategies, robust to both model uncertainties. Agent-based models of fishery economics can be implemented within the rejection framework, integrating the biological and economic understanding of the fishery. A simple artificial example of a difference equation bio-economic model is given to demonstrate the approach. Then rejection sampling is applied to an agent-based model for the hairtail (Trichiurus japonicas) fishery, where an operating model is constructed with rejection/agent-based methods and compared to known data and analyses of the fishery. The usefulness of information and rejection filters are illuminated and efficacy examined. The methods can be helpful for strategic guidance where multiple states of nature are possible as a part of management strategy evaluatio

Opportunities for agent-based modelling in human dimensions of fisheries

Models of human dimensions of fisheries are important to understanding and predicting how fishing industries respond to changes in marine ecosystems and management institutions. Advances in computation have made it possible to construct agent-based models (ABMs)—which explicitly describe the behaviour of individual people, firms or vessels in order to understand and predict their aggregate behaviours. ABMs are widely used for both academic and applied purposes in many settings including finance, urban planning and the military, but are not yet mainstream in fisheries science and management, despite a growing literature. ABMs are well suited to understanding emergent consequences of fisher interactions, heterogeneity and bounded rationality, especially in complex ecological, social and institutional contexts. For these reasons, we argue that ABMs of human behaviour can contribute significantly to human dimensions of fisheries in three areas: (a) understanding interactions between multiple management institutions; (b) incorporating cognitive and behavioural sciences into fisheries science and practice; and (c) understanding and projecting the social consequences of management institutions. We provide simple examples illustrating the potential for ABMs in each of these areas, using conceptual (“toy”) versions of the POSEIDON model. We argue that salient strategic advances in these areas could pave the way for increased tactical use of ABMs in fishery management settings. We review common ABM development and application challenges, with the aim of providing guidance to beginning ABM developers and users studying human dimensions of fisheries