Martin Zwick

Unifying the theories of inclusive fitness and reciprocal altruism.

Inclusive fitness and reciprocal altruism are widely thought to be distinct explanations for how altruism evolves. Here we show that they rely on the same underlying mechanism. We demonstrate this commonality by applying Hamilton’s rule, normally associated with inclusive fitness, to two simple models of reciprocal altruism: one, an iterated prisoner’s dilemma model with conditional behavior; the other, a mutualistic symbiosis model where two interacting species differ in conditional behaviors, fitness benefits, and costs. We employ Queller’s generalization of Hamilton’s rule because the traditional version of this rule does not apply when genotype and phenotype frequencies differ or when fitness effects are nonadditive, both of which are true in classic models of reciprocal altruism. Queller’s equation is more general in that it applies to all situations covered by earlier versions of Hamilton’s rule but also handles nonadditivity, conditional behavior, and lack of genetic similarity between altruists and recipients. Our results suggest changes to standard interpretations of Hamilton’s rule that focus on kinship and indirect fitness. Despite being more than 20 years old, Queller’s generalization of Hamilton’s rule is not sufficiently appreciated, especially its implications for the unification of the theories of inclusive fitness and reciprocal altruism.

An overview of reconstructability analysis

This paper is an overview of reconstructability analysis (RA), an approach to discrete multivariate modeling developed in the systems community. RA includes set‐theoretic modeling of relations and information‐theoretic modeling of frequency and probability distribution. It thus encompasses both statistical and nonstatistical problems. It overlaps with logic design and machine learning in engineering and with log‐linear modeling in the social sciences. Its generality gives it considerable potential for knowledge representation and data mining.

Wholes and Parts in General Systems Methodology

Reconstructability analysis (RA) decomposes wholes, namely data in the form either of settheoretic relations or multivariate probability distributions, into parts, namely relations or distributions involving subsets of variables. Data is modeled and compressed by variablebased decomposition, by more general state-based decomposition, or by the use of latent variables. Models, which specify the interdependencies among the variables, are selected to minimize error and complexity.

An Information Theory Approach to Measuring Industrial Diversification

It is demonstrated that entropy is a useful measure for examining industrial diversity either among regions or for a particular region over time. Using the entropy method, employment diversity indices are computed for the 50 states and the district of Columbia for the ten‐year period from 1972 to 1981. Of the 51 study areas, roughly half show high to moderate diversification, and none are distinguished as either highly diversified or highly specialised. Furthermore, the entropy measure is disaggregated into its between‐set and within‐set elements to express the extent and pattern of dispersal between and within different groups and subsets of industries in the United States for the 28‐year period from 1960 to 1987. The US economy is found to be relatively diversified in terms of employment over the period of study. However, there is a decreasing contribution of manufacturing and an increasing contribution of non‐manufacturing to the degree of economic diversification within the total economy.

A SOFTWARE ARCHITECTURE FOR RECONSTRUCTABILITY ANALYSIS

Software packages for Reconstructability Analysis (RA), as well as for related Log Linear modeling, generally provide a fixed set of functions. Such packages are suitable for end-users applying RA in various domains, but do not provide a platform for research into the RA methods themselves. A new software system, Occam3, is being developed which is intended to address three goals which often conflict with one another: to provide (1) a general and flexible infrastructure for experimentation with RA methods and algorithms; (2) an easily-configured system allowing methods to be combined in novel ways, without requiring deep software expertise; and (3) a system which can be easily utilized by domain researchers who are not computer specialists. Meeting these goals has led to an architecture which strictly separates functions into three layers: the Core, which provides representation of datasets, relations, and models; the Management Layer, which provides extensible objects for development of new algorithms; and the Script Layer, which allows the other facilities to be combined in novel ways to address a particular domain analysis problem.

Dynamics of prebiotic RNA reproduction illuminated by chemical game theory

Significance The origins of life required a means for information-containing molecules to compete with one another for survival and reproduction. Using an analysis based on game theory, we can predict the situations in which cooperation, selfishness, or a mixture of the two is beneficial to the future evolutionary success of RNAs. Many origins-of-life scenarios depict a situation in which there are common and potentially scarce resources needed by molecules that compete for survival and reproduction. The dynamics of RNA assembly in a complex mixture of sequences is a frequency-dependent process and mimics such scenarios. By synthesizing Azoarcus ribozyme genotypes that differ in their single-nucleotide interactions with other genotypes, we can create molecules that interact among each other to reproduce. Pairwise interplays between RNAs involve both cooperation and selfishness, quantifiable in a 2 × 2 payoff matrix. We show that a simple model of differential equations based on chemical kinetics accurately predicts the outcomes of these molecular competitions using simple rate inputs into these matrices. In some cases, we find that mixtures of different RNAs reproduce much better than each RNA type alone, reflecting a molecular form of reciprocal cooperation. We also demonstrate that three RNA genotypes can stably coexist in a rock–paper–scissors analog. Our experiments suggest a new type of evolutionary game dynamics, called prelife game dynamics or chemical game dynamics. These operate without template-directed replication, illustrating how small networks of RNAs could have developed and evolved in an RNA world.

CONTROL UNIQUENESS IN RECONSTRUCTABILITY ANALYSIS

Abstract When the reconstructability analysis of a directed system yields a structure in which a generated variable appears in more than one subsystem, information from all of the subsystems can be used in modeling the relationship between generating and generated variables. The conceptualization and procedure proposed here is discussed in relation to Klirs concept of control uniqueness

Dependence of adaptability on environmental structure in a simple evolutionary model

This article concerns the relationship between the detectable and useful structure in an environment and the degree to which a population can adapt to that environment. We explore the hypothesis that adaptability will depend unimodally on environmental variety, and we measure this component of environmental structure using the information-theoretical uncertainty (Shannon entropy) of detectable environmental conditions. We define adaptability as the degree to which a certain kind of population successfully adapts to a certain kind of environment, and we measure adaptability by comparing a populations size to the size of a nonadapting, but otherwise comparable, population in the same environment. We study the relationship between adaptability and environmental structure in an evolving artificial population of sensorimotor agents that live, reproduce, and die in a variety of environments. We find that adaptability does not show a unimodal dependence on environmental variety alone, although there is justification for preserving our unimodal hypothesis if we consider other aspects of environmental structure. In particular, adaptability depends not just on how much structural information is detectable in the environment but also on the extent to which this information is unambiguous and valuable (i.e., whether the information accurately signals a difference that makes a difference). How best to measure and integrate these other components of environmental structure remains unresolved.

Global Optimization Studies on the 1-D Phase Problem

The Genetic Algorithm (GA) and Simulated Annealing (SA), two techniques for global optimization, were applied to a reduced (simplified) form of the phase problem (RPP) in computational crystallography. Results were compared with those of “enhanced pair flipping” (EPF), a more elaborate problem-specific algorithm incorporating local and global searches. Not surprisingly, EPF did better than the GA or SA approaches, but the existence of GA and SA techniques more advanced than diose used in this study suggest that these techniques still hold promise for phase problem applications. The RPP is, furthermore, an excellent test problem for such global optimization methods.

Reconstructability Analysis of Epistasis

The literature on epistasis describes various methods to detect epistatic interactions and to classify different types of epistasis. Reconstructability analysis (RA) has recently been used to detect epistasis in genomic data. This paper shows that RA offers a classification of types of epistasis at three levels of resolution (variable‐based models without loops, variable‐based models with loops, state‐based models). These types can be defined by the simplest RA structures that model the data without information loss; a more detailed classification can be defined by the information content of multiple candidate structures. The RA classification can be augmented with structures from related graphical modeling approaches. RA can analyze epistatic interactions involving an arbitrary number of genes or SNPs and constitutes a flexible and effective methodology for genomic analysis.