From Animality to Humanity

Humans are animals. We have evolved from beings that were similar to modern apes, and those antecessors evolved from previous antecessors with features and behavior similar to modern squirrels, modern reptiles, modern amphibians, modern fishes, and modern bacteria.

There is a lot of “animality” within us, and if we want to know why we do what we are doing in the present, the only way is to understand our “degree of animality” and the historical process of differentiation from our “original” animality. This is not a defense of sociobiological approaches, but just the plain observation that we act as complex animals, and there is some kind of relationship—probably non-linear and non-monotonic—from animality to humanity. In any case, the most important aspect of investigation will not be the animal basis of human behavior, but the specific process of progressive differentiation in the way we take decisions —more or less rational—from the original animal instincts. There is no magic in this historical (prehistorical) process, but a series of explicitly mechanical biological processes that have historically constrained and determined human behavior: evolution and natural selection. Human evolution is a complex temporal trajectory of changes, transformations and modifications, some of which emerged slowly, and others very quickly. Complex phenomena in the present can be interpreted as the cumulative products of relatively simple processes acting over time.

In any case, natural selection and evolutionary mechanisms have affected ani­mals and humans not only in morphology but in the development of pre-human behavior (Premo 2005; Barton and Riel-Salvatore 2012; Pradhan et al. 2012; Witt and Schwesinger 2013; Kramer and Otarola-Castillo 2015; Tang and Ye 2016). It is also the question of the origins of “intelligence” and complex decision making (Gabora and Russon 2011; Gabora and DiPaola 2012; Kurzweil and Ray 2012; Chandrasekaran 2013; Pringle 2013; Guddemi 2014; Ross and Richerson 2014; Geary 2015; Cowley 2016) and also culture. This is not the place to define what is culture, but recent work suggests its computable basis (Belew 1990; Goodhall 2002; Richardson 2003; Bentley et al. 2004; Henrich 2004; Harton and Bullock 2007; Enquist et al. 2011; Gabora and Saberi 2011; Premo 2012, 2015; Premo and Kuhn 2010; Gabora et al. 2013; Messoudi 2011; Crema et al. 2014a, b; Acerbi et al. 2014; Cowley 2016; Gong and Shuai 2016).

An interesting example of how computer simulation may be used to test hypothesis about human evolutionary history is Agusti and Rubio-Campillo (2016). These authors deal with Neanderthals fast extinction between 40,000 and 30,000 years ago.

In this book, Ingo Timm et al. (Chap. 2) explore the possibility of simulating some aspects of hominine prehistoric behavior, notably dispersal and migration. This subject has also been approached by Mithen and Reed (2002), Beyin (2011), Eriksson et al. (2012), Wren (2014), Wren et al. (2014), Thompson et al. (2015), Holzchen et al. (2015), Kealy et al. (2015), Romanowska et al. (2016), Vahia et al. (2016). Timm et al. suggest a series of reflections for a future simulation, and not a current implementation.

Timm and co-authors have programmed their virtual human antecessors with a concrete reason to leave their original habitat, and detailed consideration of potential influencing factors. Although “animals” in the biological sense, these virtual hominins are seen as utility-based agents, considering changes in their environment and evaluating the consequences of their actions in advance. Fur­thermore, the “happiness” regarding new states created by performing an action is considered as well. Transferred to the challenges hominins faced when crossing Africa towards Eurasia, this happiness can be equated with the sufficient availability of food and other resources of vital importance. However, hominins are not the only actors which are part of the Out-of-Africa-Hypothesis that deliberate their behavior in regard to their actions. The behavior of carnivores might for example be modeled by using a similar approach as well. Choosing appropriate prey as well as selecting, defending and marking their territory are processes which can be modeled using intelligent software agents. But not all aspects of the Out-of-Africa-Hypothesis can and should be modeled as decision-making mechanisms. There are also other factors affecting the dispersal processes such as outside influences (weather or climatic changes) or the condition of the landscape (vegetation or geological for­mation).

It can be of interest to compare the dispersal mechanism of pre-humans, to the motivations and intentionality of movement and dispersal by modern humans of “prehistoric” times, with motivations different from modern humans of present times, and even our antecessors from a more recent past with motivations assumed to be like ours (Young 2002). Janssen and Hill (Chap. 3), Oestmo et al. (Chap. 4), Fort et al. (Chap. 5) and O'Brien and Bergh (Chap. 6) deal with this issue in different historical contexts. Jansen and Hill begin their analysis with the assumption that among early humans it may have existed a relationship between group size and movement and whether resources are dispersed or clumped in space, because this relationship exists and it is well attested in animal behavior. The general prediction is that movement should be less frequent in patchy environments because foragers should stay within a patch until foraging gain rates drop below some critical value before moving on. The authors explore different resource dis­tributions and how they affect optimal group size, movement frequency and average daily return rate per hunter. They also examine the effect of targeted camp move­ment (vs. random) on the return rate that can be obtained in more patchy environment.

Janssen and Hill (Chap. 3) consider the ecological parameters of the environ­ment and prey characteristics measured in the Mbaracayu Reserve, Paraguay. They have actually measured the ethnographically known Ache hunter-gatherers moving in the real world while searching for prey and other resources in any of the seven vegetation types' landscapes. Therefore, the probability of encountering a prey or a resource of a specific type can be estimated, a value that it is unknown for homi­nins, and it depends on very general assumptions.

Oestmo et al. (Chap. 4) analyze how the actual placement of resources affects hunter-gatherer movements. The authors compare random walk behavior of virtual hunter-gatherers from prehistoric times with two other walk behaviors. The first one is called “seeking walk”. During seeking walk simulations, the forager will move towards the nearest material source if the level of the materials in the toolkit is lower than a certain number. This means that at any moment when a foragers' toolkit is empty it will seek to acquire new material. The second alternative walk model is termed the “wiggle walk” where it is assumed that a forager has a direction and moves forward one cell each time step. At each time step, the forager changes the direction by taking a left turn with a degree drawn from a uniform distribution between 0° and 90°. Both the seeking walk, which is a simplified analogy for a forager that returns to a stone cache, and the random walk behavior show that increased clustering of the raw material sources leads to increased time without raw materials in the tool kit. However, time between procurement instances and time without materials in the tool kit have different implications. If a forager can stockpile a cache at a central location and can return to such a place then the forager can go extended periods without procuring because it could return to the cache to fill up on raw materials. On the other hand, these results suggest that if random walk takes the forager away from the central location and never or very seldom returns directly to a stone.

O'Brien and Bergh (Chap. 6) go forward in the investigation of the rationality of people moving. Instead of considering dispersal in a macro scale, they opt for investigating local movement in particular well known geographical areas. Strong rationality is here equated with analytically calculated Least Cost Path, as the values assigned to these models are derived from legitimate factors which influence movement, such as distaste for steep slopes, the relative difficulties of traversing different soil types, and absolute obstacles. However, these authors go well beyond the logic of “animal” movement, and they consider that social factors should not be ignored for understanding human movement, and taboos, traditions, exclusivity can be incorporated into such models. In their case study, the aim of navigating to a known settlement presupposes a minimum pre-existing cognitive map, which may be constructed from personal experience, third-party knowledge and topographical gossip. They also consider the need to include the role of a leader, and some followers. Nevertheless, they do not consider the mechanisms underlying the emergence of such differentiation. In this way, the computer simulates how route ways are established through a series of discrete actions around those natural fea­tures, acted out by individual agents over time. Modelling allows the investigation of the overall evolution of a route way as individual agents have access only to local information, allowing them to approach the optimal path over time through a process of iterative attempts to traverse a landscape. The environment of North Offaly in the Irish Midlands is used as the study area, as it is a landscape of natural route ways and obstacles for which we have rich archaeological and documentary evidence supporting interpretation of movement.

Fort et al. (Chap. 5) consider a different way to analyze human motivated movement. These authors emphasize long run movements of people at a spatial macro scale as a consequence of population increase. They consider the case study of Neolithic times, when farmers go away from their birth place when available land saturates. At a global scale the set of individual migrations can be compared with a single wave or front, advancing to neighboring areas. In this contribution, the mechanism is entirely adaptive, and no rationality, except for the intention derived from recognizing the “need” of suitable land for farming once there are no empty places in the immediate vicinity due to population increase. At this macro scale, the rationality of individual decisions can be studied in terms of the central tendency of the accumulation of individual decisions. In that way, the dispersal behavior of the population can be probabilistically based on the mean age difference between parents and their children, and a set of dispersal distances per generation and their respective probabilities.

Fort et al. contribution vindicates the mechanical nature of some apparently intrinsically human decisions: migration. At first sight, it would not be an example of the evolution of human intelligence, but a kind of animal behavior, that is, instinctive. However, in homogeneous environments it is reasonable to expect that, on average, intelligent beings will not prefer any specific direction. Obviously, this is not the single possibility. As the comparison between the different contributions on human movement in pre-industrial societies show, the intrinsic human definition lies in the historical variability of such decisions. Other authors have addressed the same subject from different perspectives (Hazelwood and Steele 2004; Goldstone and Roberts 2006; Fitzpatrick and Callaghan 2008; Bevan 2011; Callegari et al. 2013; Reynolds et al. 2013; Silva and Steele 2015; Wren 2014; Lanen et al. 2015; Sanders 2015). It is interesting to compare Fort's results with Wren's (2014) hypothesis combining a model of cognitive dispersal with the wave of advance mechanism. Wren's experiments quantify the impact of cognition on dispersal velocity and wave pattern. The results show that the greater the level of cognitive complexity, the slower the wave of advance. Increased heterogeneity of the envi­ronment further decreases wave velocity when cognition is involved in mobility. Random movement, i.e., non-cognitive mobility, provides the highest velocity across almost all landscapes. This suggests that previous research may have either overestimated the importance of cognition in facilitating dispersal events, or has underestimated the rate of population growth and per generation dispersal distance of populations. If this is a distinctive feature of pre-human populations or even Paleolithic hunter-gatherers is something that should be analyzed further, by exploring the close relationship between cognitive complexity, the spatial hetero­geneity of the landscape, and dispersal potential and velocity.

In this way we can approach the behavioral, cognitive and social consequences of evolutionary processes over the human lineages (see more discussion about those issues in Janssen et al. 2005; Griffith et al. 2010; Kempe et al. 2014; and Ackland et al. 2014; Kovacevic et al. 2015; Romanowska et al. 2016). Through the comparison of the mechanics of dispersal movements in animals, pre-humans, and humans we can arrive to understand the real impact of “intelligence” on mobility and survival in terms of an evolutionary trajectory of historically contextualized motivations and intentions.

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