A Web-based Tool for Identifying Strategic Intervention Points in Complex Systems

Abstract

Steering a complex system towards a desired outcome is a challenging task. The lack of clarity on the system's exact architecture and the often scarce scientific data upon which to base the operationalisation of the dynamic rules that underpin the interactions between participant entities are two contributing factors. We describe an analytical approach that builds on Fuzzy Cognitive Mapping (FCM) to address the latter and represent the system as a complex network. We apply results from network controllability to address the former and determine minimal control configurationssubsets of factors, or system levers, which comprise points for strategic intervention in steering the system. We have implemented the combination of these techniques in an analytical tool that runs in the browser, and generates all minimal control configurations of a complex network. We demonstrate our approach by reporting on our experience of working alongside industrial, local-government, and NGO stakeholders in the Humber region, UK. Our results are applied to the decision-making process involved in the transition of the region to a bio-based economy.

Learning versus optimal intervention in random Boolean networks

“…Firstly, the study herein suggests that optimal rulesets can be developed for networks of small to medium scale. The approach has been successfully applied to real world networks for policy-making (Moschoyiannis et al 2016), such as the transition of the South Humber Bank regional economy from fossil fuel-based to bioenergy-based products which involved a network of 8 nodes. It may not be immediately applicable to large-scale networks such as long-running transactions in digital ecosystem architectures or intelligent routing of Electric Vehicles (EVs) in city road networks (Kosmanos et al 2018), but concentrated effort and/or additional computational resources shall explore this further.…”

Route Optimization of Electric Vehicles Based on Dynamic Wireless Charging

“…Electric utilities, for example, might consider subsidizing the modification of trucks and buses into MEDs under a scenario in which the utility then becomes a revenue sharing partner with the MED owner. Governments at the state, local and national levels are all involved in policy-making decisions regarding environmental impact mitigation options, often using analytical tools [35]. In this case, governments may consider offering tax incentives to modify trucks and buses into MEDs to further promote popularity and adoption of EVs.…”

Learning Condition–Action Rules for Personalised Journey Recommendations

“…Research in this direction is limited, with recent work proposing Bayesian networks [4], heuristics and traditional routing [1] and 'case-based reasoning' [9,6], which provide customised recommendations without actual specification of passenger preferences. In previous work [8] we have applied the XCS variant [2] of LCS to study controllability [10] in Random Boolean Networks. The extension with integerbased conditions in XCSI is necessary to capture passenger preferences faithfully.…”