No abstract

Trying to meet the Sustainable Development Goals is challenging. Food supply chains may have to become more efficient to meet the increasing food requirement of 10 Billion people by 2050. At the same time, food and nutrition security are at risk from increasingly likely shocks like extreme climate events, market shocks, pandemics, changing consumer preferences, and price volatility. Here we consider some possibilities and limitations regarding the improvement of resilience (the capacity to deal with shocks) and efficiency (here interpreted as the share of produced food delivered to consumers) of food supply chains. We employ an Agent Based Model of a generic food chain network consisting of stylized individuals representing producers, traders, and consumers. We do this: 1/ to describe the dynamically changing disaggregated flows of crop items between these agents, and 2/ to be able to explicitly consider agent behaviour. The agents have implicit personal objectives for trading. We quantify resilience and efficiency by linking these to the fraction of fulfilment of the overall explicit objective to have all consumers meet their food requirement. We consider different types of network structures in combination with different agent interaction types under different types of stylized shocks. We find that generally the network structures with higher efficiency are also more sensitive to shocks, while less efficient network types display more resilience. At first glance these results seem to confirm the existence of a system-level trade-off between resilience and efficiency similar to what is reported in business management and ecology literature. However, the results are modified by the trading interactions and the type of shock. In our simulations resilience and efficiency are affected by ‘soft’ boundaries caused by the preference and trust of agents (i.e., social aspects) in trading. The ability of agents to switch between trading partners represents an important aspect of resilience, namely a capacity to reorganize. These insights may be relevant when considering the reorganization of real-life food chains to increase their resilience to meet future food and nutrition security goals.

The utility of Agent Based Models (ABMs) for decision making support as well as for scientific applications can be increased considerably by the availability and use of methodologies for thorough model behaviour analysis. In view of their intrinsic construction, ABMs have to be analysed numerically. Furthermore, ABM behaviour is o en complex, featuring strong non-linearities, tipping points, and adaptation. This easily leads to high computational costs, presenting a serious practical limitation. Model developers and users alike would benefit from methodologies that can explore large parts of parameter space at limited computational costs. In this paper we present a methodology that makes this possible. The essence of our approach is to develop a cost-e ective surrogate model based on ABM output using machine learning to approximate ABM simulation data. The development consists of two steps, both with iterative loops of training and cross-validation. In the first part, a Support Vector Machine (SVM) is developed to split behaviour space into regions of qualitatively di erent behaviour. In the second part, a Support Vector Regression (SVR) is developed to cover the quantitative behaviour within these regions. Finally, sensitivity indices are calculated to rank the importance of parameters for describing the boundaries between regions, and for the quantitative dynamics within regions. The methodology is demonstrated in three case studies, a di erential equation model of predator-prey interaction, a common-pool resource ABM and an ABM representing the Philippine tuna fishery. In all cases, the model and the corresponding surrogate model show a good match. Furthermore, di erent parameters are shown to influence the quantitative outcomes, compared to those that influence the underlying qualitative behaviour. Thus, the method helps to distinguish which parameters determine the boundaries in parameter space between regions that are separated by tipping points, or by any criterion of interest to the user.

Health care decision makers in many jurisdictions use cost-effectiveness analysis based on health economic decision models for policy decisions regarding coverage and price negotiation for medicines and medical devices. While validation of health economic decision models has always been considered important, many reviews of model-based cost-effectiveness studies report limitations regarding their validation. The current opinion paper discusses four aspects of current health economic decision modeling with relevance for future directions in model validation: increased use of complex models, international cooperation, open-source modeling, and stakeholder involvement. First, new, more complex clinical study designs and treatment strategies may require relatively complex model structures and/or input data analyses. Simultaneously, more widespread technical knowledge along with wider data availability have led to a broader range of model types. This puts extra requirements on model validation and transparency. Second, increased international cooperation of policy makers and, in particular, health technology assessment (HTA) authorities in performing model assessments is discussed in relation to the repeated use of health economic models (multi-use disease models). We argue such coordinated efforts may benefit model validity. Third, open-source modeling is discussed as one possible answer to increased transparency requirements. Finally, involvement of all relevant stakeholders throughout the whole decision process is an ongoing development that necessarily also includes health economic modeling. We argue this implies that model validity should be considered in a broader perspective, with more focus on conceptual modeling, model transparency, accuracy requirements, and choice of relevant model outcomes than previously.

half-cycle correction method provided more accurate results than calculations without any kind of half-cycle correction with the exception of one set of input parameters. ConClusions: Based on our model the most accurate method for half-cycle correction is Simpson's method as in most cases it was the closest to real data. It is important to note that with a few exceptions even the standard method's results were more accurate than in cases where no half-cycle correction was applied.