‘Till the Muddle in my Mind Have Cleared Awa’: Can We Help Shape Policy Using Systems Modelling?

Abstract

This paper considers how some well‐documented deficiencies of mental models make it difficult to create effective policies, and suggests that systems modelling can begin to address this issue. To illustrate the argument, three short cases are presented. These relate to specific domains but demonstrate how systems modelling can illuminate different general phenomena: effects on labour costs (unintended consequences and feedback), fishery management (accumulation and non‐linearity) and child protection (worldviews and sense‐making). Six levers for increasing the use of systems modelling in the policy arena are then discussed. The paper closes by emphasising the opportunities for systems modellers in the Anthropocene Era. Copyright © 2016 John Wiley & Sons, Ltd.

Unrecognized interdependencies in mental models—The case for taking feedback loops into account

“…Its representation and analysis of MMDS keep the advantages of representing models as directed graphs (Langan‐Fox et al, ) and extends the comparison methods for dealing with feedback loops (Schaffernicht & Groesser, , ). However, only a minority of studies have taken loops into account for mental model comparison (Inam, Adamowski, Halbe, & Prasher, ; Kunc, ; Lane, ; Torres & Kunc, ; Torres, Kunc, & O'Brien, ).…”

“…Most of the MoF articles also mention the term "dynamic decision making" (DDM) because the experiments expose participants to feedback-driven systems with stock-accumulation, where decisions taken at one point in time change the conditions under which decisions will be taken at later moments. Several authors carrying out DDM experiments have done so in search of inferring information about the participants' mental models (Arango et al, 2012;Gary, Pillinger, & Wood, 2012;Gary & Wood, 2011, 2016Gonzalez, Fakhari, & Busemeyer, 2017;Stouten et al, 2012;Stouten & Größler, 2017). Some researchers report that more accurate mental models have come together with increased performance in decision experiments (Capelo & Dias, 2009).…”

“…At the same time, several studies that investigate mental models in the realm of dynamic systems do so using their own way to represent mental models, focusing on the recognition of variables and links and the understanding of accumulation (Gary et al, 2012;Gary & Wood, 2011, 2016. Therefore, they do not report if the participants' mental models contained feedback loops or not.…”

A Knowledge Brokering Framework for Integrated Landscape Management

“…One approach to contextualise evidence can be in developing systems maps, e.g., representing causal loops, or stocks and flows (Lane, 2016). This can take place as part of a participatory colearning process, e.g., using workshops and interviews to build up a map (e.g., Lane, 2016;CECAN, 2019). It can also form part of a decision analysis approach.…”

Knowledge integration in One Health policy formulation, implementation and evaluation

“…These different sources of information are complementary because of missing data, methodological differences and interest-based selective perception, even among members of the same scientific team 37 , 38 . By using all of the available relevant information to understand the possible outcomes of policy interventions and by linking diverse bodies of relatively abstract information with the narratives that guide everyday experience, systems thinking can reduce uncertainty in complex governance problems 15 , 39 , 40 …”