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Emotional programs are a prime example of how natural selection designed our brains to respond to environmental challenges. Biological and psychological studies of an emotion such as fear thus tell us a great deal about how our bodies predispose us to specific kinds of religious experience. In the recent Left Behind series, apocalyptic religion elicits fear‐driven emotional responses that imbue this distinctive style of religion with characteristic moods and motivations.

This chapter gives an overview of several domains where artificial chemistries have been applied. The first example of application is in the robotics domain, where artificial chemistries have been used to control the motion of robots, in a similar way as bacteria move by chemotaxis. The second application domain is the design of distributed algorithms for computer networks, illustrated with Fraglets, a programming language inspired by chemistry, used to implement communication protocols. The third application domain is the study of natural language dynamics, either from the perspective of the evolution of human languages over history, or from the perspective of the dynamical processes of language acquisition in the brain. Music composition has also been attempted with the help of artificial chemistries, and is discussed as a forth application domain. Automated proof systems for mathematical theorems then follows. Finally an application to genetic programming is shown, where chemistry helps finding computer programs that are robust to changing the order of the instructions within them.

The subtitle of John Holland's pioneering 1975 book Adaptation in Natural and Artificial Systems correctly anticipated that the genetic algorithm described in that book would have "applications to.. .artificial intelligence." When the entities in the evolving population are computer programs, Holland's genetic algorithm can be used to perform the task of searching the space of computer programs for a program that solves, or approximately solves, a problem. This variation of the genetic algorithm (called genetic programming) enables the genetic algorithm to address the long-standing challenge of getting a computer to solve a problem without explicitly programming it. Specifically, this challenge calls for an automatic system whose input is a high-level statement of a problem's requirements and whose output is a satisfactory solution to the given problem. Paraphrasing Arthur Samuel [33], this challenge concerns "How can computers be made to do what needs to be done, without being told exactly how to do it?" This challenge is the common goal of such fields of research as artificial intelligence and machine learning. Arthur Samuel [32] offered one measure for success in this pursuit, namely "The aim [is].. .to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence." Since a problem can generally be recast as a search for a computer program, genetic programming can potentially solve a wide range of problems, including problems of control, classification, system identification, and design. Section 2 describes genetic programming. Section 3 states what we mean when we say that an automatically created solution to a problem is competitive with the product of human creativity. Section 4 discusses the illustrative problem of automatically synthesizing both the topology and sizing for an analog electrical circuit. Section 5 discusses the problem of automatically determining the placement and routing (while simultaneously synthesizing the topology and sizing) of an electrical circuit. Section 6 discusses the problem of automatically synthesizing both the topology and tuning for a controller. Section 7 discusses the importance of illogic in achieving creativity and inventiveness.

Information processing in neurons is critically dependent on dendritic morphology. The overall extent and orientation of dendrites determines the kinds of input a neuron receives. Fine dendritic appendages called spines act as subcellular compartments devoted to processing synaptic information, and the dendritic branching pattern determines the efficacy with which synaptic information is transmitted to the soma. The development of the dendritic tree is influenced by a number of factors. Studies in Drosophila have identified key components of the genetic program that regulates dendritic morphogenesis. Parallel studies in vertebrates have revealed that extracellular signals and neuronal activity exert a major influence on the growth and branching of dendrites and the formation of dendritic spines. The identification of genes that mediate these processes is providing important insight into the molecular mechanisms of dendritic morphogenesis.

The incompatibility of cultural and genetic reproduction results in anti-sexual attitudes and institutions in human social life; these may be seen as varying ways in which a socio-cultural system restricts and contains the otherwise disruptive agenda of the genetic program. Five ethnographic cases are presented in some detail to illustrate different ways in which socio-cultural systems manifest the asymmetry between the wider tribal society reproduced by the transmission of symbols, and the social units that manage the biological reproduction necessary to the society’s maintenance over generational time. Cases include the Baining, Kaulong, High Caste Nepalis, Merina, and Igbo.

The received model of evolution sees all inherited features resulting from deterministic networks of interacting genes, implying that living systems are reducible to information in genetic programs. The model requires these programs and their associated phenotypes to have evolved by an isotropic search process occurring in gradual steps with no preferred morphological outcomes. The alternative is to recognize that clusters and aggregates of cells, the raw material of evolution, constitute middle-scale material systems. This implies the necessity of bringing the modern physics of mesoscale matter into the explanatory framework for the evolution of development. The relevant, often nonlinear, physical processes were mobilized at the inception of the phyla when their signature morphological outcomes first appeared and remain as efficient causes, albeit transformed, in present-day embryos. This physicogenetic perspective reengages with concepts of saltation, orthogenesis, and environment-induced plasticity long excluded from evolutionary theory.

Information processing in neurons is critically dependent on dendritic morphology. The overall extent and orientation of dendrites determines the kinds of input a neuron receives. Fine dendritic appendages called spines act as subcellular compartments devoted to processing synaptic information, and the dendritic branching pattern determines the efficacy with which synaptic information is transmitted to the soma. The development of the dendritic tree is influenced by a number of factors. Studies in Drosophila have identified key components of the genetic program that regulates dendritic morphogenesis. Parallel studies in vertebrates have revealed that extracellular signals and neuronal activity exert a major influence on the growth and branching of dendrites and the formation of dendritic spines. The identification of genes that mediate theses processes provides important insights into the molecular mechanisms of dendritic morphogenesis.

Be it global environmental change or environment and development, landuse and land-cover change is central to the dynamics and consequences in question in the southern Yucatán peninsular region. Designing policies to address these impacts is hampered by the difficulty of projecting land use and land cover, not only because the dynamics are complex but also because consequences are strongly place-based. This chapter describes an integrated assessment modeling framework that builds on the research detailed in earlier chapters in order to project land-use and land-cover change in a geographically explicit way. Integrated assessment is a term that describes holistic treatments of complex problems to assess both science and policy endeavors in global environmental change (Rotmans and Dowlatabadi 1998). The most common form of integrated assessment is computer modeling that combines socioeconomic and biogeophysical factors to predict global climate. Advanced in part by the successes of these global-scale models, integrated assessment has expanded to structure knowledge and set research priorities for a large range of coupled human–environment problems. Increasing recognition is given to the need for integrated assessment models to address regionalscale problems that are masked by global-scale assessments (Walker 1994). Such models must address two issues to project successfully land-use and land-cover change at the regional scale. First, change occurs incrementally in spatially distinct patterns that have different implications for global change (Lambin 1994). Second, a model must account for the complexity of, and relationships among, socio-economic and environmental factors (B. L. Turner et al. 1995). The SYPR integrated assessment model, therefore, has a fine temporal and spatial grain and it places land-use and landcover change at the intersection of land-manager decision-making, the environment, and socio-economic institutions. What follows is a description of an ongoing integrated assessment modeling endeavor of the SYPR project (henceforth, SYPR IA model). The depth and breadth of the SYPR project poses a challenge to the integrated assessment modeling effort since some unifying framework must reconcile a broad array of issues, theories, and data. The global change research community offers a general conception of how environmental change results from infrastructure development, population pressure, market opportunities, resource institutions, and environmental or resource policies (Stern, Young, and Drukman 1992).