Climate shocks to food systems have been thoroughly researched in terms of food security and supply chain management. However, sparse research exists on the dependent nature of climate shocks on food-producing breadbasket regions and their subsequent cascading impacts. In this paper, we propose that a copula approach, combined with a multilayer network and an agent-based model, can give important insights on how tail-dependent shocks can impact food systems. We show how such shocks can potentially cascade within a region through the behavioral interactions of various layers. Based on our suggested framework, we set up a model for India and show that risks due to drought events multiply if tail dependencies during extremes drought is explicitly taken into account. We further demonstrate that the risk is exacerbated if displacement also takes place. In order to quantify the spatial–temporal evolution of climate risks, we introduce a new measure of multilayer vulnerability that we term Vulnerability Rank or VRank. We find that with higher food production losses, the number of agents that are affected increases nonlinearly due to cascading effects in different network layers. These effects spread to the unaffected regions via large-scale displacement causing sudden changes in production, employment and consumption decisions. Thus, demand shifts also force supply-side adjustments of food networks in the months following the climate shock. We suggest that our framework can provide a more accurate picture of food security-related systemic risks caused by multiple breadbasket failures which, in turn, can better inform risk management and humanitarian aid strategies.
Assessing the short-term socioeconomic impacts of climate-led disasters on food trade networks requires new bottom-up models and vulnerability metrics rooted in complexity theory. Indeed, such shocks could generate cascading socioeconomic losses across the networks layers where emerging agents' responses could trigger tipping points. We contribute to address this research gap by developing a multi-layer behavioral network methodology composed of multiple spatially-explicit layers populated by heterogeneous interacting agents. Then, by introducing a new multi-layer risk measure called vulnerability rank, or VRank, we quantify the stress in the aftermath of a shock. Our approach allows us to analyze both the supply-and the demand-side dimensions of the shock by quantifying short-term behavioral responses, the transmission channels across the layers, the conditions for reaching tipping points, and the feedback on macroeconomic indicators. By simulating a stylized two-layer supply-side production and demand-side household network model we find that, (i) socioeconomic vulnerability to climate-led disasters is cyclical, (ii) the distribution of shocks depends critically on the network structure, and on the speed of supply-side and demand-side responses. Our results suggest that such a multi-layer framework could provide a comprehensive picture of how climate-led shocks cascade and how indirect losses can be measured. This is crucial to inform effective post-disaster policies aimed to build food trade network resilience to climate-led shocks, in particular in more agriculture-dependent bread-basket regions.
When an economic system is subjected to a shock, be it a natural phenomenon like earthquakes or a man made calamity like wars, markets inevitably get disrupted and populations get displaced. In this scenario various markets try to stabilize themselves causing feedback effects across each other, the impact of which becomes specifically visible in sudden shifts in prices. Moreover a spatial-temporal aspect comes into play especially when disruptions happen on routes connecting markets causing delays even when supply is available. The aim of this paper is to present an agent-based model of a small economy comprising of goods and labor markets where agents interact based on certain micro dynamics developed in this paper. The interplay between these agents - the workers and capitalists - and outcomes of markets are mapped out in a framework which can be subjected to disruptions, the impact of which can be monitored at various levels of the economy. The main focus of this paper is to see how parameters in a programmed economy adjust endogenously to shocks in a controlled environment, whereas these parameters are hard to monitor and isolate in actual disaster scenarios.
Adverse post-natural disaster outcomes in low-income regions, like elevated internal migration levels and low consumption levels, are the result of market failures, poor mechanisms for stabilizing income, and missing insurance markets, which force the affected population to respond, and adapt to the shock they face. In a spatial environment, with multiple locations with independent but inter-connected markets, these transitions quickly become complex and highly non-linear due to the feedback loops between the micro individual-level decisions and the meso location-wise market decisions. To capture these continuously evolving micro–meso interactions, this paper presents a spatially explicit bottom-up agent-based model to analyze natural disaster-like shocks to low-income regions. The aim of the model is to temporally and spatially track how population distributions, income, and consumption levels evolve, in order to identify low-income workers that are “food insecure”. The model is applied to the 2005 earthquake in northern Pakistan, which faced catastrophic losses and high levels of displacement in a short time span, and with market disruptions, resulted in high levels of food insecurity. The model is calibrated to pre-crisis trends, and shocked using distance-based output and labor loss functions to replicate the earthquake impact. Model results show, how various factors like existing income and saving levels, distance from the fault line, and connectivity to other locations, can give insights into the spatial and temporal emergence of vulnerabilities. The simulation framework presented here, leaps beyond existing modeling efforts, which usually deals with macro long-term loss estimates, and allows policy makers to come up with informed short-term policies in an environment where data is non-existent, policy response is time dependent, and resources are limited.
The possible causes of vulnerability to disasters are diverse. So are its effects that can lead to a humanitarian crisis. Nevertheless, it is possible to identify certain patterns in terms of socioeconomic vulnerability, feedback effects and social tipping points that can help improve our understanding of outcomes that result from interactions in a complex system that represents a society. Based on the example of the 2005 earthquake in northern Pakistan, this paper looks at the patterns observed in a crisis scenario in more detail and analyses it using agent-based models (ABMs). This simulation-based approach aims at understanding the dynamic relationship between the individual actions of agents at the micro level and the outcomes at the macro level. The first part of the paper explains the general strengths of using ABMs for exploring the dynamics of vulnerability to crisis situations, whereas the second part focuses on the analysis of socio-economic effects of exogenous shocks in small rural-urban economies. The agent-based model presented in this paper builds upon the replicated patterns of a regional Pakistani economy and simulates the impact of a shock on migration patterns and the resulting market dynamics. The aim of this paper is to highlight the importance and efficacy of using ABMs to better understand the complex interplay of individual agents and market systems in crises situations which goes beyond structuralistic analysis and linear models.
This paper summarizes the SHELscape model, a complex systems framework developed for understanding economic transitions after natural disasters. The model is spatially defined with two agent categories (workers and owners) across two region types (rural and urban) producing two types of goods (food and a tradeable good). Seven behavioral modules define the setup of a low-income agrarian economy. A stylized calibrated system is subjected to a food production shock and changes of population, incomes, and consumption distributions are tracked. Coping mechanisms result in temporary consumption smoothing through savings; however, a large majority of the population still falls below the consumption poverty line. Two policy options, a cash transfer and a food transfer scheme, and their effects on the region are tested. Results show that income transfers result in higher income inequality while the food transfer scheme increases the rate of savings growth. The aim of this paper is to highlight how an agent-based framework can be used to study complex systems especially when data is weak and an immediate policy response is required.
This paper develops an agent-based model of a stylized low income region in order to study the impact of natural disasters on population displacement, income, prices, and consumption with a focus on distributions and coping strategies of low income groups. Key features of the model include the integration of decentralized markets into a full economy in a spatially explicit way and the analysis of short-run adjustment processes. The model is calibrated to a low income region of rural agrarian Pakistan that faced severe floods in 2010. Dynamic adaptation by agents in response to falling income includes migrating and running down savings. Despite these consumption smoothing strategies, some low income groups are vulnerable to starvation. The paper showcases two hypothetical policy scenarios, a cash and a food transfer program, and tracks their effects on the welfare of low income groups in the economy.