Suchergebnisse

Many agent-based models (ABMs) try to explain large-scale phenomena by reducing them to behaviors at lower scales. At these scales in social systems are functional groups such as households, religious congregations, coops and local governments. The intra-group dynamics of functional groups often generate inefficient or unexpected behavior that cannot be predicted by modeling groups as basic units. We introduce a framework for modeling intra-group decision-making and its interaction with social norms, using the household as our focus. We select phenomena related to women's empowerment in agriculture as examples influenced by both intra-household dynamics and gender norms. Our framework proves more capable of replicating these phenomena than two common types of ABMs. We conclude that it is not enough to build multi-scale models; explaining social behaviors entails modeling intra-scale dynamics.

Home to over half the world's population, cities are the drivers of the global economy and the primary influencers of the Earth's sustainability. Thus, the burden of sustainable economic development falls ever more on cities, with many global organizations and governments calling for the promotion of 'green' economies. Yet how does a city move from its current economic structure to a green economy? Using detailed occupational data for US cities, we develop a green jobs index based on the network of interdependencies between occupational specializations. Using this index we quantify how close a city's current economy is to the green economy. We further show that movement or transition through this 'occupation space' toward a green economy is a slow and difficult process, with the average annual movement towards a green economy across all US cities being close to zero. Such difficulty is uncorrelated with a city's current population size, density, per capita GDP, per capita income, or even the city's current green jobs index. Furthermore, the structure of occupational interdependencies gives rise to suboptimal movements towards the green economy.

AbstractResilience is increasing rapidly as a framework to understand and manage coupled human–natural systems. Yet the concept of resilience is rarely quantified. Here we quantify system resilience by operationalizing the notion of system tightness. Multiple resilience frameworks recognize the strong relationship between system tightness and resilience, though they differ on the directionality of that relationship. Thus, by measuring the system tightness we ultimately measure urban economic resilience, with the added benefit of empirically determining the directionality of the relationship between tightness and resilience. We then assess how well this measure predicts the response of urban economies to the recent so-called Great Recession. Results show that cities with lower tightness (higher resilience) fared better during the recession with respect to several economic productivity measures. However, in the absence of shocks, those with higher tightness (lower resilience) exhibit superior economic performance. Thus, a tradeoff between efficiency and resilience is nicely reflected in the empirical data. Although this study deals with economic shocks, quantitative metrics based on its methodology may help anticipate a city's response to shocks more generally, such as natural disasters, climate change, social unrest or significant policy shifts.