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Agent-based Modeling of Policies to Improve Urban Food Access for Low-income Populations

Despite advances in medical technology and public health practices over the past few decades, there has been a steady increase in the prevalence of chronic diseases like type 2 diabetes among low-income urban residents in the US. For this population, maintaining a diet consisting of nutritious foods is complicated by a number of physical and social barriers. In cities, a coalescence of social, spatial, and economic factors influence the availability of healthy food in any given place. The urban food environment contextualizes the structural and individual-level norms that drive daily decision-making about what to eat. Understanding and acting on the processes that reduce these residents' access to healthy foods will make for a healthier urban landscape. A new paper by M.J. Widener, S.S. Metcalf, and Y. Bar-Yam advances the discussion of food deserts by using an agent-based model to simulate the impact of various policy interventions on low-income households’ consumption of fresh fruits and vegetables.

 

M.J. Widener, S.S. Metcalf, and Y. Bar-Yam, Agent-based Modeling of Policies to Improve Urban Food Access for Low-income Populations. Applied Geography. 40 pp. 1-10. 2013.

http://www.necsi.edu/research/social/urbanfood/policies/

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The ultimate guide to memory - New Scientist

We are all collections of memories. They dictate how we think, act and make decisions, and even define our identity.

Yet memory, with its many virtues and flaws, has puzzled for centuries. How are memories made and stored in the brain? Why do we remember some events but not others? What do other animals remember? And how can we improve the flawed instrument handed to us by evolution?

In these articles we answer these questions and many more, starting with a revolutionary new understanding of memory’s purpose.


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Complex dynamics of elementary cellular automata emerging in chaotic rules

Complex dynamics of elementary cellular automata emerging in chaotic rules | Papers | Scoop.it

We show novel techniques of analysing complex dynamics of cellular automata (CA) with chaotic behaviour. CA are well known computational substrates for studying emergent collective behaviour, complexity, randomness and interaction between order and disorder. A number of attempts have been made to classify CA functions on their spatio-temporal dynamics and to predict behavior of any given function. Examples include mechanical computation, lambda and Z-parameters, mean field theory, differential equations and number conserving features. We propose to classify CA based on their behaviour when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behaviour from almost any initial condition. Thus in just a few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory.

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