Santa Fe Institute

About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Genetic Programming II: Automatic Discovery of Reusable Programs.

Abstract: Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed because the book without appendices is about 650 pages. An entire previous book by the same author [1] is devoted to describing Genetic Programming (GP), while this book is a sequel extolling an extension called Automatically Defined Functions (ADFs). The author, John R. Koza, argues that ADFs can be used in conjunction with GP to improve its efficacy on large problems. "An automatically defined function (ADF) is a function (i.e., subroutine, procedure, module) that is dynamically evolved during a run of genetic programming and which may be called by a calling program (e.g., a main program) that is simultaneously being evolved" (p. 1). Dr. Koza recommends adding the ADF technique to the "GP toolkit." The book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. This is stated as Main Point 1. Main Point 2 states that ADFs work by exploiting inherent regularities, symmetries, patterns, modularities, and homogeneities within a problem, though perhaps in ways that are very different from the style of programmers. Main Points 3 to 7 are appropriately qualified statements to the effect that, with a variety of problems, ADFs pay off be-

Why social networks are different from other types of networks

Abstract: We argue that social networks differ from most other types of networks, including technological and biological networks, in two important ways. First, they have nontrivial clustering or network transitivity and second, they show positive correlations, also called assortative mixing, between the degrees of adjacent vertices. Social networks are often divided into groups or communities, and it has recently been suggested that this division could account for the observed clustering. We demonstrate that group structure in networks can also account for degree correlations. We show using a simple model that we should expect assortative mixing in such networks whenever there is variation in the sizes of the groups and that the predicted level of assortative mixing compares well with that observed in real-world networks.

The lack of a priori distinctions between learning algorithms

Abstract: This is the first of two papers that use off-training set (OTS) error to investigate the assumption-free relationship between learning algorithms. This first paper discusses the senses in which there are no a priori distinctions between learning algorithms. (The second paper discusses the senses in which there are such distinctions.) In this first paper it is shown, loosely speaking, that for any two algorithms A and B, there are “as many” targets (or priors over targets) for which A has lower expected OTS error than B as vice versa, for loss functions like zero-one loss. In particular, this is true if A is cross-validation and B is “anti-cross-validation” (choose the learning algorithm with largest cross-validation error). This paper ends with a discussion of the implications of these results for computational learning theory. It is shown that one cannot say: if empirical misclassification rate is low, the Vapnik-Chervonenkis dimension of your generalizer is small, and the training set is large, then with high probability your OTS error is small. Other implications for “membership queries” algorithms and “punting” algorithms are also discussed.

Foundations of Human Sociality - Economic Experiments and Ethnographic: Evidence From Fifteen Small-Scale Societies

Abstract: 1. Introduction and Guide to the Volume 2. Overview and Synthesis 3. Measuring Social Norms and Preferences Using Experimental Games: A Guide for Social Sciences 4. Coalitional Effects on Reciprocal Fairness in the Ultimatum Game: A Case from the Ecuadorian Amazon 5. Comparative Experimental Evidence from Machiguenga, Mapuche, Huinca, and American Populations Shows Substantial Variation Among Social Groups in Bargaining and Public Goods Behavior 6. Dictators and Ultimatums in an Egalitarian Society of Hunter-Gatherers - the Hadza of Tanzania 7. Does Market Exposure Affect Economic Game Behavior? The Ultimatum Game and the Public Goods Game Among the Tsimane of Bolivia 8. Market Integration, Reciprocity, and Fairness in Rural Papua New Guinea: Results from a Two-Village Ultimatum Game Experiment 9. Ultimatum Game with an Ethnicity Manipulation: Results from Khovdiin Bulgan Sum, Mongolia 10. Kinship, Familiarity, and Trust: An Experimental Investigation 11. Community Structure, Mobility, and the Strength of Norms in an Africa Society: the Sangu of Tanzania 12. Market Integration and Fairness: Evidence from Ultimatum, Dictator, and Public Goods Experiments in East Africa 13. Economic Experiments to Examine Fairness and Cooperation among the Ache Indians of Paraguay 14. The Ultimatum Game, Fairness, and Cooperation among Big Game Hunters

The return of the variance: intraspecific variability in community ecology

Abstract: Despite being recognized as a promoter of diversity and a condition for local coexistence decades ago, the importance of intraspecific variance has been neglected over time in community ecology. Recently, there has been a new emphasis on intraspecific variability. Indeed, recent developments in trait-based community ecology have underlined the need to integrate variation at both the intraspecific as well as interspecific level. We introduce new T-statistics ('T' for trait), based on the comparison of intraspecific and interspecific variances of functional traits across organizational levels, to operationally incorporate intraspecific variability into community ecology theory. We show that a focus on the distribution of traits at local and regional scales combined with original analytical tools can provide unique insights into the primary forces structuring communities.