Hunting-and-Gathering in the Past Explains How We Have Survived Until the Present

Previous discussion on simulating movement and dispersal among pre-humans and humans at different periods of history reveal the strong naturalistic character of many human decisions, and the constraints imposed by environment.

Prehistoric hunter-gatherers have been studied many times from the point of view of animal foraging behavior, stating that human agents also forage in such a way as to maximize their net energy intake per unit time. In other words, it is assumed they should find, capture and consume food containing the most calories while expending the least amount of time possible in doing so. This is the old Malthusian view on population increasing exponentially while food production would have increased only linearly, in constant increments (Portugali 1999; Read and LeBlanc 2003; Lane 2010; Cai 2012; Schlueter et al. 2012; Levin et al. 2013; Hritonenko and Yatsenko 2013; Ribeiro 2015). Consequently, population growth would have generated on the long term the depletion of “natural capital”, and declining biodiversity. Since these trends undermine the probabilities for survival, when “human load” exceeds local carrying capacity it erodes environmental potential. These concerns were the first to attract the interest of archaeologists who found the possibility of the computer modelling of hunter and gatherer survival (Zubrow 1971; Thomas 1972; Wobst 1974; Joachim 1976). The understanding of many ecological concepts such as adaptation, energy flow and competition hinges on the ability to comprehend what food items such human agents selected, and why. Nevertheless, it is obvious that if humans were in the past just like any other animal forager or predator, we would say that prehistoric hunter-gatherers survival would have depended just on the availability of edible resources.

The hypothetical explanation of “adaptive” mechanisms in human prehistory should be much deeper than that. For instance, in the case of gathering, we can assume that posterior probabilities for gathering success, and hence of survival, may be completely defined by the probability of plants availability. In case the environment is full of available resources (“rich world hypothesis”), the probability of finding enough plants to eat and make instruments is very high, and prior probabilities for survival are also high; in the case of low availability, prior prob­abilities for survival would be lower. Hunting seems to be a much more complex activity, whose success and hence the posterior probabilities of survival are less deterministically affected by the availability of animals in the area. If a social agent cooperates with another agent, the chances of hunting success are higher, even in the case of low animal availability, and so on. Availability of technology can also increase posterior probabilities of survival even in the case of low prior priors due to scarcity. Therefore, a successful explanation of hunting and gathering survival in prehistory needs additional factors and dependencies to be able to calculate pos­terior probabilities of survival (Del Castillo and Barcelo 2013; Barcelo et al. 2015).

The single most obvious constraint of human action in a particular environment is population size, especially when the means of production seem to be underde­veloped (hunting-and-gathering). Many modern computer simulations on human demography are centered on modeling the particular dependence on annual fertility tables and adopt a fecundity based model. The odds of conception for any one mating event can be kept constant for a female agent of a given age, and the probability of reproduction therefore becomes dependent on the frequency and timing of the female agent's mating activity.

How simple and well adapted to the local carrying capacity is population growth in a hunting gathering economic system? Whereas the demands of non-human species on their habitats are fixed and limited, human demands, even during the most remote period of our past, have been hardly simple and are constantly evolving. Chapman (1980), Samuels (1982), Read (1998), Costopoulos (2002) have created social reproduction models based on modern ethnography of hunters and foragers groups, taking into account the social and political aspects of marriage and complex way of reproductive tasks scheduling influenced by political and ideo­logical goals.

Smaldino et al. (2013) investigate the evolution of a population under conditions of different environmental harshness and in which selection can occur at the level of the group as well as the level of the individual. The authors focus on the evolution of a socially learned characteristic related to individuals' willingness to contribute to raising the offspring of others within their family group.

White (2013, 2014, 2016) has built an Agent Based Model representing a hunter-gatherer system taking into account parameters such as mortality, fertility, and mean age. The demographic characteristics of a living population are the result of numerous human-level interactions and behaviors: persons and households make decisions about marriage and reproduction based on their individual circumstances within the context of “global” conditions that exert effects and constraints on all members of the population (e.g., the physiological factors that govern the length of the female reproductive span, ecological circumstances that affect the contributions of children to subsistence, cultural rules affecting marriage behaviors, etc.). The demographic characteristics of these systems (e.g., population age structure, mean fertility, mean mortality) emerge through a large number of human level interac­tions and behaviors related to marriage, reproduction, and mortality. The model has three main “levels”: person, household, and system. Each agent in the model represents an individual person who is a discrete entity with a unique identity. Households are co-residential groupings of persons that form through marriage and change in size and composition primarily through marriage, reproduction, and mortality. Social links define relationships between pairs of living persons and are used to enforce marriage prohibitions. The system of the model is composed of all persons and households in existence at a given point in time. Methods representing marriage, reproduction, and death operate at the person and household levels in this model. Individual persons and households make probabilistic decisions about reproduction, marriage, and infanticide based on the current dependency ratio of the household (the ratio of the number of consumers to the number of producers in the household).

Crema (2014) assumes that human groups are characterized by a non-linear relationship between size and per-capita fitness. Increasing group size has beneficial effects, but once a certain threshold is exceeded, negative frequency dependence will start to predominate leading to a decline in the per-capita fitness. Such a relationship can potentially have long-term implications in the spatial structure of human settlements if individuals have the possibility to modify their fitness through group fission-fusion dynamics. He illustrates the equilibrium properties of these dynamics by means of an abstract agent-based simulation and discusses its impli­cation for understanding long-term changes in human settlement pattern.

The second part of the problem when trying to couple the social and the envi­ronmental lies in modeling carrying capacity and the capability of prehistoric humans, even with inefficient technology to alter and modify it. Demographic and expansion behaviours of groups are largely influenced by the distribution and availability of resources. This has been an important domain for research on computer modeling and much effort is still being invested (Keane et al. 2002; Sept 2007; Seth 2007; Wainwright 2008; Garfinkel et al. 2010; Janssen 2010; Dearing et al. 2012; Van der Bergh et al. 2013; Ch'ng et al. 2013; Marean et al. 2015; Millington et al. 2013; Burch et al. 2014; Jones and Richter 2014; Balbo et al. 2014; Barton et al. 2014; Feola 2014; Bentley and O'Brien 2015; Codding and Bird 2015; Rammer and Seidl 2015; Rodriguez et al. 2015; Wood et al. 2015; Iwamura et al. 2016; Boumans et al. 2015; Polhill et al. 2016; Sarjoughian et al. 2016). The problem is that human-nature systems have been traditionally studied separately, either as human systems constrained by or with input from/output to natural sys­tems (usually including the physical environment and the corresponding ecosys­tem), or as natural systems subject to human disturbance. This chasm between natural and social sciences, along with such unidirectional connections between natural and human systems, has hindered better understanding of complexity (e.g., feedback, nonlinearity and thresholds, heterogeneity, time lags). In the process of truly coupling human activity and natural environment, computer simulation approaches allow understanding how human decisions and subsequent actions would change (at least affect) the structure and function of many natural systems. Such structural and functional changes would in turn exert influence on human decisions and actions (An 2012; Widlock et al. 2012; Sarjoughian et al. 2015). In this sense, Dorward (2014) proposes a ‘livelisystems' framework of multi-scale, dynamic change across social and biological systems. This describes how material, informational and relational assets, asset services and asset pathways interact in systems with embedded and emergent properties undergoing a variety of structural transformations. Related characteristics of ‘higher' (notably human) “livelisystems” and change processes are identified as the greater relative importance of (a) infor­mational, relational and extrinsic (as opposed to material and intrinsic) assets, (b) teleological (as opposed to natural) selection, and (c) innovational (as opposed to mutational) change. This suggestion provides valuable insights into the real understanding of 99 % of human history, when survival was only possible through hunting and gathering.

We may wonder about the unbalanced application of simulation, where the biological side (as in human evolution) has greatly benefitted from simulation while the more “sociological” aspect of archaeological simulation remains a challenge (Lake 2014; Cegielski and Rogers 2016). To understand the coupling between human and environmental systems in prehistory, researchers should study human collective behavior as a consequence of the indirect influence individual agents and organized populations of agents may have had on other hunter gatherers given that each one responds to an environment altered by the behavior of other agents. The general purpose of this way of studying prehistory seems to be the simulation of potential historical situations in which agents periodically may have modified their output behavior when they were able to learn to predict how the action at a previous step modifies the input at the next step. Many individuals can end up near each other simply because they tend to approach the same localized resource such as food or a water source. In these circumstances too, the agents' behavior resulting in social aggregation has not evolved for that function. Each individual approaches food or water for eating or drinking, not for social purposes. However, even if it is a simple by-product of learning nonsocial behaviors, social aggregation can be a favorable pre-condition for the emergence of social behaviors such as communi­cation and economic exchange among individuals that happen to find themselves near each other. In other circumstances, however, social aggregation may not be simply a by-product of behavior emerged for other purposes but is the result of behavior which has emerged exactly because it produces spatial aggregation (Lake 2000; Costopoulos 2001; Berman et al. 2004; Goldstone and Ashpole 2004; Goldstone et al. 2005a, b; Parisi and Nolfi 2005; Janssen and Ostrom 2006; Kalff et al. 2010; Barton et al. 2011; An 2012; Rounsevell et al. 2012; Ch 'ng and Gaffney 2013; Boone and Galvin 2014; Messoudi 2014; Clark and Crabtree 2015).

Related to this debate, in the present book, Saqalli and Baum (Chap. 8) consider that humans have historically formed complex groups and societies that are bound to their environment in more or less intense interactions, the imprint of which are found in landscapes. A society and its evolution can be studied as driven by their calorie and resource demand and constrained by environmental parameters. Thus, archaeological/paleo-environmental models can either directly analyze the social interactions between agents, or use the landscape as a reference plane. In any case, it is the mutual interdependence of humans and their environment that is in the focus: environment and natural resources are quickly and directly affected by human activities and at the same time, humans are directly and rapidly affected by the availability of natural resources.

However, it is important to take into account that not any measured differences in survival between individuals through time reflect necessary differences in fitness Brookfield (2001). Fitness represents an expected outcome, and what actually happens in small populations differs from expectation because each generation represents a sample, with an attendant sampling error, of the individuals produced by the previous generation. The fitness of a population is related only probabilis­tically to real events; sudden advantageous changes and transformations are usually lost by chance.

Janssen and Hill (Chap. 3), and Oestmo et al. (Chap. 4) have modelled the particular way in which human prehistoric behavior can be considered as “adapted” to environmental conditions (see also Read 2008; Kline and Boyd 2010; Collard et al. 2011; Kuhn 2012; Wood et al. 2015; Caiado et al. 2016; Martin and Fahrig 2016). In the first case, Janssen and Hill examine how optimal group sizes and movement frequency are affected by more dispersed or more clumped resource distributions, when the absolute number of resources in the environment is held constant. They also examine the effect of targeted camp movement (vs. random) on the return rate that can be obtained in more patchy environment. The model uses real measured parameters from a modern foraging society to create an agent-based model, which subsequently allows simulating a more or less patchy environment in order to determine how those changes affect optimal group size and mobility. They conclude that human foragers, by knowing the landscape and the spatial location of better habitats, and moving to facilitate hunting in those areas, can gain a substantial advantage from that knowledge. In the other contribution, Oestmo et al., investigate whether changes in stone tool raw material frequencies in an archaeological assemblage could be considered a reliable proxy for human forager adaptive variability. Two different patterns are obtained in their simulated model. First, when a forager engages in random or wiggle walk, a more clustered environment leads to lower average raw material richness in the toolkit. As clustering increases, the forager will on average move longer periods without encountering a source. Due to this and the fact that the forager use a material at every step, the forager will then when encountering a source fill up the tool kit to the maximum capacity resulting in one raw material dominating the make-up of the tool kit in terms of frequency. In the other pattern, the forager engages in a seeking walk and seeks the closest raw material sources when the tool kit is empty. In this case, the increased clustering of raw material sources leads to increased raw material richness. The richness increases because when the forager seeks the nearest raw material source, and this nearest raw material source is clustered with other sources, it increases the chance of encountering other sources in close proximity that in turn could lead to increased richness.

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