Simulation-Based Participatory Modelling in Urban and Production Logistics: A Review on Advances and Trends

Simulation-based participatory modelling allows integration of all types of knowledge including empirical, technical and scientific from all disciplines and domains. Thus, in recent years, the use of participatory modelling has been continuously increasing in many fields including logistics. With a view of achieving better understanding of the subject, this article systematically reviews the advances made in participatory modelling in the field of urban and production logistics in the last decade. It further reports the findings transparently following a categorization based on (i) the purpose of participatory modelling in the domain, and (ii) depending on the purpose how data is collected, processed and outcomes are presented. The review resulted in 97 articles which were analysed and categorized based on the above two questions formulated in the literature surveyed. The review revealed that apart from the three existing categories of purposes, namely, reflexive, descriptive and normative there is an emerging fourth category that was analytical in nature and 15 out of 97 articles analyzed belonged to this category. The authors decided to call this category analytical field empirical modelling which is primarily based on mathematical modelling and use of computational methods. We present these results with the help of a categorization. During the analysis for the second research question, we discovered that though the conventional ways of data collection and processing, such as interviews and workshops, which remain significantly present, in electronic data crowdsourcing and data processing via computational methods are emerging.

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.

RCaN: a software for Chance and Necessity modelling

Uncertainty is a challenge in modelling ecological systems and has been a source of misunderstandings between modelers and non-modelers. The "Chance and Necessity" (CaN) modelling approach has been proposed to address this issue, in the case of trophic network modelling. CaN modelling focuses exploring food-web trajectories that can satisfy fundamental physical and biological laws, while being compatible with observations and domain knowledge. This type of approach can facilitate discussion among actors as it promotes sharing of information and does not presuppose any knowledge of modelling practices. It is therefore suitable for participatory modelling , i.e. a modelling approach in which different actors can confront their knowledge and understanding of the marine system and of the associated uncertainties. One important ingredient to achieve participatory modelling is the availability of a modelling platform that is efficient, fast and transparent, so that all actors can understand and follow the modelling steps and choices, and can rapidly visualize and discuss the results. But, until now, there existed no software to easily perform CaN modelling. Here, we present RCaN and RCaNconstructor. Combined, these provide the first tool to build CaN models in an intuitive way that is 1) suitable within participatory frameworks, 2) transparent, 4) computationally efficient, 5) fully documented through the provision of meta-information and 6) supportive of exploratory analyses through predefined graphical functions.

PARTICIPATORY MODELLING OF THE CAUSAL RELATIONSHIP BETWEEN SELF-BELIEFS AND PERSONALITY USING AGGREGATED FUZZYRELATIONAL MAPMODEL AND KOSKO-HAMMING DISTANCE

Fuzzy Relational Map is an efficient tool in establishing the causal relationship between two disjoint sets of concepts. In situations, wherein the data available is unsupervised involving emotions and reasons described in a language that is vague or difficult to interpret, Fuzzy Relational Map is the pertinent approach of choice. Personality is a psychological construct that has different traits and these traits have some unique behavioral beliefs underneath. In this paper, an extension of a fuzzy relational map called aggregatedFuzzyRelational Map is used to study the association between the traits of personality and the behavioral beliefs that influence a certain type of personality. Further, the fixed points are analyzed with the aid of Kosko-Hamming Distance.

A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making

Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here, we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data and interpreting model findings from a decision-maker’s perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO EMR, as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesising and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.

A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making

Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office (WHO EMRO) recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data, and interpreting model findings from a decision-maker’s perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO Eastern Mediterranean Region (EMR), as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesizing and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.