Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process.

Participatory modeling engages the implicit and explicit knowledge of stakeholders to create formalized and shared representations of reality and has evolved into a field of study as well as a practice. Participatory modeling researchers and practitioners who focus specifically on environmental resources met at the National Socio-Environmental Synthesis Center (SESYNC) in Annapolis, Maryland, over the course of 2 years to discuss the state of the field and future directions for participatory modeling. What follows is a description of 12 overarching groups of questions that could guide future inquiry.Participatory approaches to resource management must involve those who are affected by the decisions that stem from environmental management decisions (Reed et al., 2009). Environmental resource management often requires a combination of descriptive and normative knowledges as well as local capacity for action (and inaction). Because resource users have such knowledge and capacity, local engagement is crucial in PM. However, there are imbricated layers of power between researchers and locals-often with researchers holding the balance of power due to their increased access to social goods such as money, formal education,

The literature on policy interventions to address obesogenic dietary behaviors can be divided into 3 distinct categories. The first is the use of economic measures to alter food consumption, such as taxing unhealthy ingredients and subsidizing healthy foods, 1---4 in light of studies that have shown the price of a calorie obtained from unhealthful foods is lower than the price of a calorie from more healthful foods. 5,6 The second is targeting the food environment through zoning polices, including increasing the number of healthy food vendors in "food desert" communities 7 and restricting the opening of new fast-food restaurants. 8The third and final category is related to combating unhealthy eating norms, given research showing the power of food marketing to change dietary behaviors and proposing restrictions on the time, place, and manner in which obesogenic foods are marketed. 9,10 Conversely, pro-nutritional marketing focuses on education as a means of increasing consumers' awareness of dietary health (e.g., nutrition disclosure on menus and the issuing of dietary guidelines). 11, 12The research to date focusing on the effects of these various approaches has incorporated theoretical and empirical techniques that rely on the stable unit treatment value assumption, according to which there are no interactions among people who experience an intervention that would alter the effectiveness of the intervention. However, this assumption is known to be violated in the case of obesity-related behaviors.13, 14 What is therefore not clear from existing regression-based and experimental empirical work is the potential magnitude of the population-level impact of these policies if they were implemented in the real world. We performed simulations to contrast the potential of different approaches aiming at tackling unhealthy dietary behaviors in a population of urban US adults. Simulations involving systems dynamics or agent-based modeling (ABM) are increasingly being used in public health, 15,16 We developed a computer model explicitly representing how individuals make decisions to examine the impact of policies on their food consumption. Our model was based on a multilevel theory of population health that emphasizes the role of cognitive habits in human behaviors. 25 According to this theory, individual beliefs are influenced by incentivesas in rational-choice theory-but also shaped by cognitive habits that are reinforced within a population by social norms and culture. Thus, our model examined how individual beliefs are influenced by interventions either in the social network or in the food environment. METHODSOur agent-based model contained 2 kinds of agents: individuals and food outlets. Individuals make dietary choices, and food outlets adapt to those choices. In our model, a single time period was defined as 1 day, and each simulation was run for more than 3 years (1100 days).In our simulations, individuals were assigned demographic characteristics (age, gender, Objectives. Unhealthy eating is a complex-system prob...