Cognition and hypocognition: discursive and simulation-supported decision-making within complex systems

Homo sapiens is currently believed to have evolved in the African savannah several hundreds of thousands of years ago. Since then, human societies have become, through technological innovation and application, powerful influencers of the planet's ecological, hydrological and meteorological systems – for good and ill. They have experimented with many different systems of governance, in order to manage their societies and the environments they inhabit – using computer simulations as a tool to help make decisions concerning highly complex systems, is only the most recent of these. In questioning whether, when and how computer simulations should play a role in determining decision-making in these systems of governance, it is also worth reflecting on whether, when and how humans, or groups of humans, have the capability to make such decisions without the aid of such technology. This paper looks at and compares the characteristics of natural language-based and simulation-based decision-making. We argue that computational tools for decision-making can and should be complementary to natural language discourse approaches, but that this requires that both systems are used with their limitations in mind. All tools and approaches – physical, social and mental – have dangers when used inappropriately, but it seems unlikely humankind can survive without them. The challenge is how to do so

Measuring heterogeneity in soil networks:a network analysis and simulation-based approach

Quantifying soil structural and ecological heterogeneity is crucial for understanding their interactions and their relationships to the resilience and health of the wider ecosystem. However, a clear understanding of how structural heterogeneity affects soil biodiversity is still emerging. Previous work has primarily used expensive, often laboratory-based methods to quantify soil pore network structure, and typically separated study of structural and biological dimensions. Here, we test whether standard network metrics can be used to quantify structural heterogeneity in soil pore networks, and how this network structure, along with characteristics of the consumer and resource populations, affects the heterogeneity of a population of consumers. Specifically, we extract simplified soil pore networks from digital photographs of soil profiles and apply established metrics from network science and transport geography to quantify and compare the networks. The networks are also used as the medium for an agent-based model of generalised consumers, to analyse the effects of consumer and resource parameterisations and network structure. Combining network analysis and simulation modelling in this way can provide insights on the structure, function, and diversity possible in the soil, as well as avenues for exploring the impact of future structural or environmental changes

Co-evolution of network structure and consumer inequality in a spatially explicit model of energetic resource acquisition

Energetic resources in ecological and social–ecological systems are distributed through complex networks, which co-evolve with the system and consumers to move resources from points of origin to those of end use. Past research has focused on effects of spatiotemporal resource heterogeneity in ecosystems and society, or socioeconomic drivers of inequality, with less attention to interactions between resource network structure and population-level outcomes. Here, we develop a spatially explicit, stock-flow consistent agent-based model of generic consumers building and crossing links between resources, and we explore the co-evolution of the emergent network structure and inequality in consumers’ resource reserves across three distinct landscapes. We show that the consumer inequality initially decreased during network expansion, then increased rapidly as the network reached a more stable state. The spatial distribution of resources in each of the three landscapes constrained the structures that could emerge, and therefore the specific rates and timings of these dynamics. This work demonstrates the use of energetically consistent modelling to understand possible relationships among a spatially distributed set of resources, the network structure that connects them to a population, and inequality in that population. This can provide a theoretical underpinning informing further work to better understand causes of resource inequality and heterogeneity in observed systems

It's not the 'what', but the 'how':Exploring the role of debt in natural resource (un)sustainability

<div><p>A debt-based economy cannot survive without economic growth. However, if private debt consistently grows faster than GDP, the consequences are financial crises and the current unprecedented level of global debt. This policy dilemma is aggravated by the lack of analyses factoring the impact of debt-growth cycles on the environment. What is really the relationship between debt and natural resource sustainability, and what is the role of debt in decoupling economic growth from natural resource availability? Here we present a conceptual Agent-Based Model (ABM) that integrates an environmental system into an ABM representation of Steve Keen’s debt-based economic models. Our model explores the extent to which debt-driven processes, within debt-based economies, enhance the decoupling between economic growth and the availability of natural resources. Interestingly, environmental and economic collapse in our model are not caused by debt growth, or the debt-based nature of the economic system itself (i.e. the ‘<i>what</i>’), but rather, these are due to the inappropriate use of debt by private actors (i.e. the ‘<i>how</i>’). Firms inappropriately use bank credits for speculative goals–rather than production-oriented ones–and for exponentially increasing rates of technological development. This context creates temporal mismatches between natural resource growth and firms’ resource extraction rates, as well as between economic growth and the capacity of the government to effectively implement natural resource conservation policies. This paper discusses the extent to which economic growth and the availability of natural resources can be re-coupled through a more sustainable use of debt, for instance by shifting mainstream banking forces to partially support environmental conservation as well as economic growth.</p></div

Exploring sustainable scenarios in debt-based social-ecological systems: The case for palm oil production in Indonesia

A debt-based economy requires the accumulation of more and more debt to finance economic growth, while future economic growth is needed to repay the debt, and so the cycle continues. Despite global debt reaching unprecedented levels, little research has been done to understand the impacts of debt dynamics on environmental sustainability. Here, we explore the environmental impacts of the debt-growth cycle in Indonesia, the world's largest debt-based producer of palm oil. Our empirical Agent-Based Model analyses the future effects (2018-2050) of power (im)balance scenarios between debt-driven economic forces (i.e. banks, firms), and conservation forces, on two ecosystem services (food production, climate regulation) and biodiversity. The model shows the trade-offs and synergies among these indicators for Business As Usual as compared to alternative scenarios. Results show that debt-driven economic forces can partially support environmental conservation, provided the state's role in protecting the environment is reinforced. Our analysis provides a lesson for developing countries that are highly dependent on debt-based production systems: sustainable development pathways can be achievable in the short and medium terms; however, reaching long-term sustainability requires reduced dependency on external financial powers, as well as further government intervention to protect the environment from the rough edges of the market economy

Projecting the effect of crop yield increases, dietary change and different price scenarios on land use under two different state security regimes

Peer reviewedPublisher PD

Modelling food security: Bridging the gap between the micro and the macro scale

Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.</p

Agent-based modelling as a method for prediction for complex social systems

No abstract available

Machine-assisted agent-based modeling: Opening the black box

While agent-based modeling (ABM) has become one of the most powerful tools in quantitative social sciences, it remains difficult to explain their structure and performance. We propose to use artificial intelligence both to build the models from data, and to improve the way we communicate models to stakeholders. Although machine learning is actively employed for pre-processing data, here for the first time, we used it to facilitate model development of a simulation model directly from data. Our suggested framework, ML-ABM accounts for causality and feedback loops in a complex nonlinear system and at the same time keeps it transparent for stakeholders. As a result, beside the development of a behavioral ABM, we open the ‘blackbox’ of purely empirical models. With our approach, artificial intelligence in the simulation field can open a new stream in modeling practices and provide insights for future applications

Trajectories toward maximum power and inequality in resource distribution networks

Resource distribution networks are the infrastructure facilitating the flow of resources in both biotic and abiotic systems. Both theoretical and empirical arguments have proposed that physical systems self-organise to maximise power production, but how this trajectory is related to network development, especially regarding the heterogeneity of resource distribution in explicitly spatial networks, is less understood. Quantifying the heterogeneity of resource distribution is necessary for understanding how phenomena such as economic inequality or energetic niches emerge across socio-ecological and environmental systems. Although qualitative discussions have been put forward on this topic, to date there has not been a quantitative analysis of the relationship between network development, maximum power, and inequality. This paper introduces a theoretical framework and applies it to simulate the power consumption and inequality in generalised, spatially explicit resource distribution networks. The networks illustrate how increasing resource flows amplify inequality in power consumption at network end points, due to the spatial heterogeneity of the distribution architecture. As increasing resource flows and the development of hierarchical branching can both be strategies for increasing power consumption, this raises important questions about the different outcomes of heterogeneous distribution in natural versus human-engineered networks, and how to prioritise equity of distribution in the latter. © 2020 Davis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited