The Broken Village: Coffee, Migration, and Globalization in Honduras

[Excerpt] This book describes how people cope with rapid social change. It tells the story of the small town of La Quebrada, Honduras, which, over a five-year period from 2001-2006, transformed from a relatively isolated community of small-scale coffee farmers into a hotbed of migration from Honduras to the United States and back.1 During this time, the everyday lives of people in La Quebrada became connected to the global economy in a manner that was far different, and far more intimate, than anything they had experienced in the past. Townspeople did not generally view this transformation as a positive step toward progress or development. They saw migration as a temporary response to economic crisis, even as it became an ever more inescapable part of their livelihood. The chapters that follow trace the effects of migration across various domains of local life — including politics, religion, and family dynamics — describing how individuals in one community adapt to economic change. This is not a story about an egalitarian little Eden being corrupted by the forces of capitalist modernization. La Quebrada\u27s residents have lived with social inequality, violence, political conflict, and economic instability for generations. As coffee farmers, their fortunes have long been tied to the vicissitudes of global markets. However, the social changes wrought by migration presented qualitatively new challenges, as a functioning local economy became dependent on migrants working in distant places such as Long Island and South Dakota who lived in ways that most people in La Quebrada struggled to comprehend or explain. The new reality of migration created a sense of confusion that was especially strong in the early stages of La Quebrada\u27s migration boom, when communication between villagers and migrants was rare. The decline of coffee markets and the rise of the migration economy happened so quickly and chaotically that people struggled to understand, evaluate, and give meaning to the changes they wereexperiencing. Therefore, migration was experienced as sociocultural disintegration in 2003-2005, when the bulk of the research for this study was conducted

String Matching: Communication, Circuits, and Learning

String matching is the problem of deciding whether a given n-bit string contains a given k-bit pattern. We study the complexity of this problem in three settings. - Communication complexity. For small k, we provide near-optimal upper and lower bounds on the communication complexity of string matching. For large k, our bounds leave open an exponential gap; we exhibit some evidence for the existence of a better protocol. - Circuit complexity. We present several upper and lower bounds on the size of circuits with threshold and DeMorgan gates solving the string matching problem. Similarly to the above, our bounds are near-optimal for small k. - Learning. We consider the problem of learning a hidden pattern of length at most k relative to the classifier that assigns 1 to every string that contains the pattern. We prove optimal bounds on the VC dimension and sample complexity of this problem

On giant components and treewidth in the layers model

Given an undirected $n$-vertex graph $G(V,E)$ and an integer $k$, let $T_k(G)$ denote the random vertex induced subgraph of $G$ generated by ordering $V$ according to a random permutation $\pi$ and including in $T_k(G)$ those vertices with at most $k-1$ of their neighbors preceding them in this order. The distribution of subgraphs sampled in this manner is called the \emph{layers model with parameter} $k$. The layers model has found applications in studying $\ell$-degenerate subgraphs, the design of algorithms for the maximum independent set problem, and in bootstrap percolation. In the current work we expand the study of structural properties of the layers model. We prove that there are $3$-regular graphs $G$ for which with high probability $T_3(G)$ has a connected component of size $\Omega(n)$. Moreover, this connected component has treewidth $\Omega(n)$. This lower bound on the treewidth extends to many other random graph models. In contrast, $T_2(G)$ is known to be a forest (hence of treewidth~1), and we establish that if $G$ is of bounded degree then with high probability the largest connected component in $T_2(G)$ is of size $O(\log n)$. We also consider the infinite two-dimensional grid, for which we prove that the first four layers contain a unique infinite connected component with probability $1$