Social Dynamics of Littering and Adaptive Cleaning Strategies Explored Using Agent-Based Modelling

In this paper we explore how social influence may cause a non-linear transition from a clean to a littered environment, and what strategies are effective in keeping a street clean. To study this, we first implement the Goal Framing Theory of Lindenberg and Steg (2007) in an agent based model. Next, using empirical data from a field study we parameterise the model so we can replicate the results from a field study. Following that, we explore how different cleaning strategies perform. The results indicate that an adaptive/dynamical cleaning regime is more effective and cheaper than pre-defined cleaning schedules

Modeling social norms in real-world agent-based simulations

Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans\u27 actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing

Models as mindtools for environmental education: How do students use models to learn about a complex socio-environmental system?

Environmental issues are complex and understanding them involves integration of different areas of knowledge, feedback and time delays, however strategies to cope with complexity are not often used or taught in environmental education. The aim of this thesis is to examine the benefit of three such strategies for environmental education: multiple external representations, learning from models, and collaborative learning. The socio-environmental system modelled was visitor impact in a national park in Australia. Students in Year 9 and 10 from two schools were given a text description (Text group) and either a system dynamics model (SDM group), an agent-based model (ABM group), or both models (SDM & ABM group). This experimental design allowed learning outcomes (environmental and system dynamics knowledge, and understanding of the socio-environmental system) and use of the model(s) (in terms of the proportion of time spent on each screen, activities, and strategies) to be compared in each learning environment (individual and collaborative). Multiple external representations were the most successful strategy in the individual learning environment in terms of increases in environmental knowledge. However, students given only the system dynamics model had greater understanding of the system, and students given only the agent-based model increased environmental knowledge easily identified in the animated representation. Prior knowledge, patterns of use, strategies for changing variables and the representational affordances of the models explained some of these differences. In particular, prior knowledge was an important indicator of how students coordinated use of the models in the SDM & ABM group. Learning with a system dynamics model was the most successful strategy for students in the collaborative learning environment. Differences between the learning environments were detected in all groups with respect to both learning outcomes and use of the models due to prior knowledge, interrogation of the models, and the learning environments themselves. These experiments have provided evidence that strategies for understanding complex systems provide viable methods of communicating complex ideas to school-aged students with varying levels of prior knowledge. In particular, multiple external representations provided students with flexibility in how they learned; models allowed students to experiment with a system otherwise not allowed; and a collaborative learning environment facilitated students’ interpretation of a system dynamics model

The implementation of a waste regulatory framework in the city of Johannesburg

A research report submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand in 50% fulfillment of the requirements for the degree of Master of Management (in the field of Public and Development Sector Monitoring and Evaluation) May 2017High volumes of illegally dumped waste and littering in the City of Johannesburg have legal, socio-economic and environmental implications and are also an indicator of ineffective waste regulatory framework implementation. Inadequate waste regulation enforcement suggests poor waste governance that impacts negatively on urban management. This research sought to identify the underlying reasons for this, drawing on data obtained from documents and interviews with representatives of the City of Johannesburg and members of the community. The findings revealed four broad themes that should be considered for effective implementation of the waste regulatory framework: the waste governance model, waste regulation measures, leadership in waste management, and public value for waste services. Factors that contributed to ineffective implementation included lack of leadership to drive waste policy and plans, waste governance characterised by poor relations amongst actors, lack of solidarity, lack of trust and reciprocity, lack of mutual support and shared sense of purpose, inadequate intellectual capital required for effective waste policy implementation, and limited power to mobilize both financial and tangible resources to fulfil the waste policy mandate and obligation. The high volume of illegally dumped waste throughout the City as well as high levels of littering reflect weaknesses in urban management and governance of the City. This discourages potential investors that are critically needed to promote growth, since the cleanliness of a City and the effectiveness of its solid waste management system are used as a proxy indicator of good governance.MT 201

Collaborative water-resource governance in the UK: Understanding network structure and functionality of a catchment-based approach to water-quality management

Since 2011 water resource governance in the UK has begun to integrate a collaborative multi-stakeholder approach to water-quality management. The Catchment-Based Approach (CaBA) facilitates local partnerships of stakeholders to co-create plans, align actions, and make collective decisions about efforts to improve and protect local river and stream environments. The approach offers potential for the enactment of effective, equitable and sustainable water management, but it is often unclear how such efforts are characterised practically. The multiplicity of stakeholders and complexity of issues and influences contribute to difficulty in discerning how governance change is functioning. This thesis uses a case study of the River Wear Catchment, North East England, where stakeholders have been operating CaBA, to begin to explore the patterns and drivers of actions and interactions that facilitate collaborative water-resource governance at the stakeholder level. Drawing on the concept of the catchment as a complex, social-environmental system, this research utilises insights from stakeholders and a combination of analytical methods, including a network approach and agent-based modelling, to provide new perspectives on the network structure and functioning of multi-stakeholder water management. A network approach is used to build a picture of interactions amongst stakeholders and to reveal the nature of the new relationships built through CaBA. Qualitative analysis of interview data identifies key influences on the decision-making of stakeholders and the functionality of new and existing networks of relations at three levels; the interactional, individual and contextual. Agent-based modelling is then used as a heuristic research tool to combine knowledge of relational structures with influences on stakeholder behaviour to experiment with potential dynamics of the system through a specific water-quality, problem-based scenario. The combination of these analytical methods allows a more in-depth and dynamic understanding of the patterns and processes of CaBA than has been revealed previously. The thesis ultimately comments on the utility of such methods for creating new understandings of the operationalisation of water governance processes, and for the utility of those new understandings to inform and question the facilitation of effective and satisfactory delivery of collaborative multi-stakeholder water-quality management at the catchment-scale

Crossing the chasm: a 'tube-map' for agent-based social simulation of policy scenarios in spatially-distributed systems

Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Partly, this is an issue of awareness – ABM is still new enough that many people have not heard of it; partly, it is an issue of confidence – ABM has more to do to prove itself if it is to become a preferred method. This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Using Geoffrey A. Moore’s Crossing the Chasm as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises. This leads us to identify several areas where work is needed

Simulating human behaviour in social-ecological systems: farmers’ adoption of agricultural innovations

The agricultural sector faces major challenges to produce sufficient food for the world’s rising population. Addressing these challenges requires a transformation towards sustainable agriculture. Although sustainable agricultural practices such as agroforestry provide various benefits, small-scale farmers’ uptake of such practices can be very low in certain regions. Consequently, interventions are required that support farmers’ implementation and raise low adoption rates. Small-scale farmers’ adoption of sustainable agricultural practices and the underlying decision-processes constitute the core of this thesis. Overall, the thesis aims to support policy-makers in developing and implementing effective measures that encourage farmers to adopt innovative sustainable practices. The specific objectives are (1) to identify efficient information seeding strategies to disseminate agricultural knowledge within social networks, (2) compare common behavioural approaches to explain farmers’ adoption decisions, (3) identify intrinsic drivers based on the Theory of Planned Behaviour and evaluate the effectiveness of non-economic policy interventions targeting intrinsic motivational factors, and (4) assess the interrelated human-environmental consequences of farmers’ adoption decisions under different climate scenarios