James Dow (2008)
Is Religion an Evolutionary Adaptation?
Journal of Artificial Societies and Social Simulation
vol. 11, no. 2 2
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Received: 01-May-2007 Accepted: 29-Jan-2008 Published: 31-Mar-2008
(1) Cognitive theories that postulate that religion is the manifestation of mental modules1 that have evolved for other purposes (Atran and Norenzayan 2004; Boyer 2001, 2003).
(2) Ecological regulation theories that postulate that religion is a master symbolic control system regulating the interaction of human groups with their environments, and, therefore, it has evolved as an adaptive mechanism with this function (Rappaport 1999).
(3) Commitment theories that postulate that religion is a system of costly signals that reduce deception and create trust and cooperation within groups (Irons 2001; Sosis 2004).
Ecological regulation theory and commitment theory propose that religion has evolved as an adaptive mechanism, while cognitive theory does not.
There is a growing consensus among social scientists that the capacity for religion is carried in the human brain and much of that capacity is there at birth (Wolpert 2006; McNamara 2006; King 2007); however, that brain does not have to understand its own adaptive workings. The rational brain has evolved to solve immediate problems of survival. It analyzes sensory input to find solutions to problems that the sensory organs detect in the environment. However, it has no need to analyze long-term evolution. Thus, the hidden evolutionary adaptiveness of religion is obscure to the rational brain. Evolution, itself, organizes long-term adaptations, and the rational brain does not deal with them.
Evidence for evolutionary adaptation can exist either as:
(1) a correlation between a behavior and a particular environment or as
(2) a valid model of an evolutionary processes that selects the behavior.
Sociobiology proposes that genes and culture do not evolve independently, on separated, isolated tracks. The neurobiology of human mental development makes them co-dependent, resulting in the process of gene-culture coevolution (Lumsden and Wilson 1981). Gene-culture coevolution in human beings appears to be based on gene-culture transmission, a process of organismic growth and development in which innate learning capacities respond to certain forms or types of cultural information in preference to others, demarcating the central tendencies around which cultural diversity plays (Lumsden 1999).
|Figure 1: Gene-culture effects on an agents behavior. Genetic effects in red. Cultural learning in blue.|
|Figure 2: Population increase in Case 2.|
|Figure 3: The evolving means of real (black) and unreal (blue) communication capacities in Case 2|
|Figure 4: Population increase in five runs of Case 2.|
|Figure 5: The evolving means of real (black) and unreal (blue) communication capacities in five runs of Case 2.|
|Figure 6: The growth in population caused by better learning in Case 3.|
|Figure 7: The evolving means for real (black) and unreal (blue) communication capacities in Case 3.|
|Figure 8: Longer term population growth with more cost to costly signaling.|
|Figure 9: Case 4: Final distribution of real communication capacity (ipr) with more cost to costly signaling. The probability ranges are in tenths.|
|Figure 10: The trend of means of the capacities to make real (black) and unreal (blue) communications in Case 4.|
|Figure 11: Population decline and growth in Case 5.|
|Figure 12: The evolving selection of capacities for real (black) and unreal (blue) communications with the greenbeard effect in operation.|
|Figure 13: The final distribution of the capacity for real communication (ipr) in Case 5. The capacity ranges are in tenths.|
|Figure 14: The final distribution of the capacity for unreal communication (ipu) in Case 5. The capacity ranges are in tenths.|
The author acknowledges the help and encouragement received from Candace Alcorta, Richard Sosis, Lawrence Kuznar, and the editor and reviewers of the JASSS.
1. The term module is commonly used in evolutionary psychology to describe a pattern of heritable behavior aimed at solving a particular problem of survival in the evolutionary past (Barkow, Tooby, and Cosmides 1992).
|1||ns = 140; ip1 = 10; le = 65; mc = 5; dcr = 0.00; dcu = 0.00; dcur = 0.00; dfir = 0.017; dfiu = -0.005; dfisu = -0.01; dp = 0; lr = 0; ur = 1; lv = 0; uu = 1; dist = 0; popgrow = 0; greenbeard = 0; mult = 0; cmr = 0.5; cmu = 0.5; icp = 0.2;|
|2||ns = 140; ip1 = 10; le = 65; mc = 5; dcr = 0.01; dcu = 0.01; dcur = 0.01; dfir = 0.012; dfiu = -0.005; dfisu = -0.01; dp = 0; lr = 0; ur = 1; lv = 0; uu = 1; dist = 0; popgrow = 0; greenbeard = 0; mult = 0; cmr = 0.5; cmu = 0.5; icp = 0.2;|
|3||ns = 140; ip1 = 10; le = 65; mc = 5; dcr = 0.01; dcu = 0.01; dcur = 0.01; dfir = 0.012; dfiu = -0.005; dfisu = -0.01; dp = 0; lr = 0; ur = 1; lv = 0; uu = 1; dist = 0; popgrow = 0; greenbeard = 0; mult =1; cmr = 0.5; cmu = 0.5; icp = 0.2;|
|4||ns = 300; ip1 = 10; le = 65; mc = 5; dcr = 0.01; dcu = 0.01; dcur = 0.01; dfir = 0.010; dfiu = -0.005; dfisu = -0.02; dp = 0; lr = 0; ur = 1; lv = 0; uu = 1; dist = 0; popgrow = 0; greenbeard = 0; mult =1; cmr = 0.5; cmu = 0.5; icp = 0.2;|
|5||ns = 140; ip1 = 10; le = 65; mc = 5; dcr = 0.01; dcu = 0.01; dcur = 0.01; dfir = 0.010; dfiu = -0.005; dfisu = -0.02; dp = 0; lr = 0; ur = 1; lv = 0; uu = 1; dist = 0; popgrow = 0; greenbeard = 1; mult =1; cmr = 0.5; cmu = 0.5; icp = 0.2;|
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