Table 1
Overview of foraging models: Summary of all analysed foraging models and their properties.
| SOURCE | NONAKA AND HOLME (2007) | GRIFFITH ET AL. (2010) | JANSSEN AND HILL (2014) | WREN ET AL. (2018) | WREN ET AL. (2020) | GRAVEL-MIGUEL ET AL. (2022) | RODRíGUEZ ET AL. (2023) | KOPELS AND ULLAH (2024) | SEURU ET AL. (2024) |
|---|---|---|---|---|---|---|---|---|---|
| Model ID | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 |
| Keywords | – | Paleoecology, Agent-based models, Human evolution | Optimal foraging theory, Agent-based modelling | agent-based modelling, optimal-foraging theory, middle stone age, marine foraging | Holocene, Paleogeography, Southern Africa, Data treatment, Data analysis, Agent-based modeling | Agent-based model, Coastal adaptation, Middle stone age, South Africa, Paleoscape model, Hunter-gatherers | – | Megafauna extinction, anthropogenic impacts, social-ecological systems, complex adaptive systems, agent-based modeling, South Africa | Agent-based model Social division of labor European rabbit Net-hunting Optimal Foraging Theory Upper Palaeolithic Iberia |
| Timeframe | Gatherer | Plio-Pleistocene hominids | Ache Hunter-gatherer | Middle Stone Age | Holocene | pre-agricultural Holocene | Late Pleistocene | Late Quaternary | Last Glacial Maximum |
| Spatial scale | – | 8,000 cells with each cell 10,000 m2 | 58,408 cells with each cell 10,000 m2 | 60,000 cells with each cell 10,000 m2 | 421,200 cells with each cell 10,000 m2 | 60,000 cells with each cell 10,000 m2 | 2,691 cells with each cell 1 km2 | 44,521 cells | 2,640,000 cells with each cell 0.01 km2 |
| Time scale | 100,000 ticks | 262,800 ticks per simulation, with each tick representing one minute. | Tick = 5 minutes, 100 years model run | Each tick represents 1 day | Each tick represents 1 day | 365 ticks per simulation, with each tick representing one foraging day of varying length. | 9,000 ticks per simulation, with each tick representing one hour. | 73,000 ticks with each tick representing a day | 18,250 ticks with each tick representing a day |
| Spatial explicitness | Abstract environment | Two maps representing the Voi and Turkana regions. Each cell is classified as either channel, flooded, or unflooded. | Map of the Mbaracyu Forest Reserve, divided into cells representing one of seven major vegetation classes. | A map depicting a section of South Africa, divided into 14 terrestrial and coastal habitats. | A map depicting a section of South Africa, divided into 14 terrestrial and coastal habitats. | A map of the South Cape of South Africa featuring fourteen different habitat types, based on the reconstructed pre-modern distribution of terrestrial vegetation and habitats in South Africa. | Abstract homogeneous landscape. | Abstract map featuring cells which are either vegetated or vegetated. | A map of north-eastern Iberia featuring elevation and habitat suitability. |
| Resource complexity | A general resource that varies in density depending on the scenario and regenerates over time. | Cells contain edible plants that regrow during the growth season. Each topographic zone has an associated daily probability of carcass appearance. | 26 prey species with varying hunting-related attributes (e.g., pursuit time, weight) and individual population dynamics. | Each cell in the map provides a caloric return rates of harvesting, time required to harvest, current state of depletion, and time until replenishment, which vary based on habitat type and is influenced by seasonal and tidal cycles. | Each cell is assigned associated variables relating to the caloric return rates of resources, time required for resource exploitation, current state of resource depletion, and time until replenishment based on habitat type and is influenced by seasonal and tidal cycles. | Each habitat type includes estimated caloric returns from plants or shellfish, time required to gather these resources, and density of animal prey. The environment experiences seasonal and tidal changes. | Predators move randomly throughout the environment and produce carcasses stochastically. The frequency of carcass production and the nutrient content (energy) vary by predator species, based on reconstructed data. | Cells can once be exploited by grazing animal agents. Grazed cells regrow after a specified number of time steps. Animal agents provide energy units. | Each cell has information about the number of individuals per cell, and information about each prey type including: its energetic value in kilocalories, the mean handling time to acquire it and its breeding rate |
| Agent Levels | Individual forager | Individual hominids | Individual hunters and Camps | Agents represent individual foragers or camps | Agents represent individual foragers or camps | Band agents represent groups of foragers. | Agents represent groups of hominins, predators, or scavenging species. | Band agents representing groups of foragers. | Individual humans and Camps |
| Foraging strategies | An agent that consumes the general resource by following an optimal foraging strategy. | Individual hominids consume plants and animals based on their daily caloric needs and dietary restrictions, following an optimal foraging approach. The energy gained depends on local searching processes and methods used by the hominids. | Individual hunters operate from camps that focus solely on hunting. Prey are ranked by “profitability” based on expected meat yield per hour of pursuit (kg). Only prey types with profitability above recent mean hunting returns are targeted. | Individual foragers gather plants and shellfish using an optimal foraging approach to meet their daily energy needs. | Individual foragers gather or hunt using an optimal foraging approach to meet their daily energy needs. | Foraging agents must meet a daily energy demand through gathering plants, shellfish, and hunting mammals using an optimal foraging approach; 30% are designated as hunting agents, with pursuit time varying by prey type. | Hominin agents must meet a daily energy requirement. They only scavenge and always target the nearest available carcass when necessary. | Hunting | Hunting and catching at warrens. |
| Movement model | The forager can either travel between resource patches or forage within a single patch. | Hominid agents can travel to any of their 8 neighbouring cells at equal cost, taking one timestep to move to a neighbouring cell. | Camps relocate based on specific scenarios (1, 2, 4, 8, or 16 days). Foragers spend their days acquiring resources and moving to new camp locations. | Every Day foragers move in the direction of the new camp location while using the available time to gather resources. | One randomly chosen hunter picks a direction all hunters follow. | Individual foraging agents navigate the landscape and may relocate their camp if their caloric threshold is not met. | Hominins move at a speed of 5 km/h. | Direct movement towards the closest, highest-ranked prey animal within their foraging radius. Without nearby animals the agents perform random-walk. | Direct movement towards the best habitable cell within their mobility range. |
| Sensing | The forager has awareness of the resources available in their immediate foraging area. | Hominid agents can assess the energetic return of all plants in their vicinity and detect all carcasses within their detection range. | Agents are only aware of the conditions in the cell they occupy. | Agents can assess expected caloric returns from patches within a specified radius; for coastal patches, this evaluation can extend over greater distances. Camps can predict the future availability of resources. | Gatherers can assess the current return rate of patches within a specified radius and coastal patches even if they are outside of the radius. Hunters can assess the probability of encountering specific species per habitat type. | Agents can possess knowledge about the state of all cells or only within a specific vision radius regarding future resource availability. They select the cell that offers the highest net caloric return based on acquired energy. | Hominins can sense available carcasses within their range of vision. | The agents can assess the estimated energetic return of all prey animals within their foraging radius (6 or 12 cells) | The agents can assess the habitat suitability of all cells within their mobility range. The mobility range depends on the movement speed and the available time. |
| Ingroup interactions | – | Agents may choose to nest together as a cooperative strategy. Group-nesting agents may relocate if several members have not met their caloric demands. | Hunters within the same camp stay close to each other and may cooperate during hunting encounters. | – | Hunters follow a common direction. | Cooperation allows foragers to compete effectively against other scavengers. | – | Individual agents share acquired resources in the camp. | |
| Group decision making | – | When relocating, they follow the agent that previously acquired the most food, especially after failing to meet energy demands over a specified period. | New camp locations are chosen randomly from cells located at least 2 km away from the current position. | The group moves strategically to maximize caloric returns. | The camps move if they failed to acquire the needed calories over the last seven days. | Agents chose the Cell with the highest net calory return | Hominin groups will move to the closest cell containing a carcass if they require new energy. | Agents always target the closest, highest-ranked prey animal. | The group moves if the caloric needs of its 25 members have not been met over the last seven days. |
| Population dynamics | – | – | – | Population size can be determined at the start of each simulation run. | Population size can be determined at the start of each simulation run. | Agents may die if they fail to acquire sufficient energy but do not reproduce. | – | – | |
| Intergroup interactions | – | – | Hunters from different camps do not interact with each other. | – | – | – | Competition for resources occurs; if multiple agents attempt to exploit the same carcass, the species size or group size determines which agent succeeds. | – | – |
| Responses | The energy gained and the average return per unit of time spent foraging are tracked. | Records include calories consumed per resource and season, as well as idle time spent in camp and the type of vegetation inhabited. | Average return in kg, percentage of time spent searching, percentage of days without a catch, and prey composition. | Average caloric returns, days without food, and ratios of different food types. Key mobility factors include frequency of camp movement, distance travelled per camp or forager, types of vegetation occupied, and time spent near the coast. | Average caloric returns, days without food, and ratios of different food types. Key mobility factors include frequency of camp movement, distance travelled per camp or forager, types of vegetation occupied, and time spent near the coast. | Metrics include average caloric return per time spent foraging, calories consumed per resource, and seasonal variations in resource availability. | Survival success is measured by the total population at the end of the simulation, | Number of prey-items taken per 10 ticks, Proportion of grazed to ungrazed grass, Forager energy and Number of animals | Daily energetic intake per human and the number of individuals hunted from each prey type. |
| Large-scale connectivity | – | Illustrates changes in land usage based on environmental conditions and access to tools. | Suggested population sizes are provided based on the characteristics of the studied area. | Suggested population sizes are provided based on the characteristics of the studied area. | Small but productive coastal habitats are often reoccupied over extended periods. | The results indicate that passive scavenging may be a highly effective strategy for early Pleistocene hominins in Europe. | – | – | |
| Open-Source Code | No | – | https://www.comses.net/codebases/3902/releases/1.1.0/ | https://www.comses.net/codebases/5356/releases/1.0.0/ | https://www.comses.net/codebases/2d6a597a-76af-4ee8-bf90-0ba8c531b686/releases/1.0.0/ | https://www.comses.net/codebases/7bbb91d3-e455-4afd-82b2-a62c94ed1aef/releases/1.0.0/ | https://figshare.com/articles/code/SCAVCOMP-ABM_A_computer_model_to_simulate_competition_among_scavengers/22716427 | https://github.com/isaacullah/MegafaunaHuntingPressure/tree/main | https://www.comses.net/codebases/584b56bd-13b4-4896-9d4b-336777cf2437/releases/2.0.0/ |
| Programming Language | – | Java | Netlogo 5.0.3 | NetLogo 5.3.1 | Netlogo 6.0.3 | NetLogo v. 6.1.1 | Net Logo 6.2.2 | NetLogo 6.3.0 | NetLogo 6.0.3 |
Table 2
Overview of central-place models: Summary of all analysed central-place models and their properties.
| SOURCE | PREMO (2012) | PREMO (2015) | SIKK AND CARUSO (2020) |
|---|---|---|---|
| Model ID | CP1 | CP2 | CP3 |
| Keywords | Foraging; Human evolution; Hunter-gatherer Mobility; Simulation; Spatial model | Human evolution; Hunter-gatherers; Metapopulation model, Mobility, Modern human behavior | agent-based model, hunter-gatherers, central place foraging, mobility, settlement choice |
| Timeframe | Late Pleistocene | Modern Human/Central-Place Foragers | General Hunter-gatherers |
| Spatial scale | 40,000 cells | 62,500 cells | 10,000 cells with each cell 1 km2 |
| Time scale | 5,000 ticks per simulation | 2,000 ticks per simulation, with each tick representing an abstract period required for social learning. | 80 ticks per simulation, with each tick representing one week. |
| Spatial explicitness | Abstract map featuring a randomly selected subset of cells that provide resources | An abstract map featuring cells that provide resources. | An abstract map featuring cells that provide varying amounts of potential energy based on available technology and social organisation. |
| Resource complexity | One generic resource that regenerates every 1,000-time steps | One generic resource that regenerates every 800-time steps. | One generic resource that regenerates every 4 ticks at a specified rate. |
| Agent Levels | Each agent represents a foraging group | Each agent represents a foraging group. | Each agent represents a residential unit consisting of 20 individuals. |
| Foraging strategies | Agents completely exploit one cell per time step | Agents completely exploit one cell per time step. | Agents exploit cells based on their population size. |
| Movement model | Agents can gather food within their effective foraging radius; residential camps move a distance of 2re + 1 | Agents can procure food within their effective foraging radius. Follows a correlated random walk with a step length of 2 × Log + 1. | Agents procure food from surrounding areas following the optimal foraging approach. Agents will relocate if the time required to meet their energy needs exceeds the time needed to move to a new site, factoring in relocation costs. |
| Sensing | Foragers can only detect resources within their designated foraging range | Foragers can only detect resources within their foraging range. | Agents can only detect the energy levels and net energy returns of cells within their residential range. |
| Ingroup interactions | Group activities are coordinated by the group agent to reflect collective decision-making | Group activities are conducted by the group agent to represent collective decision-making. | Active foragers influence the decision-making process regarding resource acquisition. |
| Group decision making | – | – | Agents select new camp locations based on the highest maximum energy return available in nearby cells. |
| Population dynamics | – | – | – |
| Intergroup interactions | – | Cultural variances can be transmitted between groups when they are within the interaction radius. | – |
| Responses | Residential moves and interactions occur between two agents | Residential moves, cultural variance and time until fixation of one culture | Residential moves per year and the duration of logistical forays during each residential stay (averaged across all agents). |
| Large-scale connectivity | A population primarily employing a logistical mobility strategy exhibits fewer intergroup interactions | Populations primarily using logistical mobility strategies may exhibit higher cultural diversity due to rare intergroup cultural transmissions. | Resource distribution impacts mobility: however, settlement locations are influenced by factors beyond just available energy. |
| Open-Source Code | https://www.comses.net/codebases/3582/releases/1.0.0/ | https://www.comses.net/codebases/5038/releases/1.0.0/ | |
| Programming Language | NetLogo 5.0.2 | NetLogo 5.0.2 | NetLogo 6.04 |
Table 3
Overview of social-interaction models: Summary of all analysed social-interaction models and their properties.
| SOURCE | SANTOS ET AL. (2015) | PEREDA ET AL. (2017) | COTO-SARMIENTO ET AL. (2023) |
|---|---|---|---|
| Model ID | SI1 | SI2 | SI3 |
| Keywords | Whales, Meat, Social influence, Random Walk, Trees, Agent-based modelling, Animal sociality, Foraging | – | Pleistocene, Central Asia, behavioural adaptations, evolutionary model, human cooperation, Agent-Based Model |
| Timeframe | Human hunter-gatherer | Human hunter-gatherer | Hominins during the Pleistocene |
| Spatial scale | 40,401 cells | 256 cells | 175,000 cells with each cell 1 km2 |
| Time scale | 100,000 ticks per simulation, with each tick representing an abstract period of days, weeks, or months during which a whale may beach. | 5,000 ticks per simulation, with each tick representing an abstract period between a new generation | 1,800 ticks per simulation, with each tick representing one month. |
| Spatial explicitness | An abstract water landscape featuring additional land cells surrounded by beach cells. | An abstract space in which foragers find resources depending on the chance to find resources. | The area surrounding the Altai Mountains and the Tian Shan region is mapped. Each cell is characterized by either water or mountains and contains a certain amount of resources. |
| Resource complexity | Randomly selected beach cells may contain beached whales. | Acquiring resources sorely depends on the chosen probability of finding resources. Each found resource provides one unit of energy. | All patches provide a general resource that is randomly distributed within a range of 0 to 50. Some cells offer higher resource amounts that regenerate over time (referred to as “attractor places”). |
| Agent Levels | Agents represent households or canoes that forage for resources. | Individual foragers | Individual foragers |
| Foraging strategies | Foragers exclusively exploit beached whales as their resource. | Agents have a chance to find an abstract resource. | Individual foragers acquire resources, with the amount consumed varying based on their cooperative strategy. |
| Movement model | Agents utilize random walk or Lévy flight movement, covering distances between 1 and 13 cells. | Agents do not move | Foragers will migrate if they cannot find available resources within a specified period or if the resource levels in their current patches fall below 20%. |
| Sensing | Agents can sense beached whales within a specified vision range. | Agents sense if other agents have not acquired any resources. | Agents can sense nearby resources after becoming familiar with their surroundings. Environmental knowledge is shared among agents. |
| Ingroup interactions | – | Successful foragers share resources either with the one unsuccessful forager that has previously shared the most resources or with a random unsuccessful forager. | – |
| Group decision making | – | – | – |
| Population dynamics | – | – | Agents remaining in unsafe locations for extended periods are at risk of hypothermia and eventual death. Agents age and die based on an average mortality rate, with a reproduction rate of 10%. |
| Intergroup interactions | Agents have the option to cooperate with other agents by sharing information about beached whales, which rewards them with reputation points. | – | During difficult periods, cooperating agents ration and share resources to support one another. Agents can choose to consume fewer resources and collaborate with other cooperators to establish new attractor places. |
| Responses | The average number of cooperating agents within the population is tracked. | Mean energy of all Agents and the composition of the sharing behaviour. | The number of cooperators and surviving agents is tracked. Cooperative behaviour increases the likelihood of survival during harsh environmental conditions. |
| Large-scale connectivity | Aggregation events, such as the presence of beached whales, promote intergroup interactions and increase cooperation within the metapopulation. | – | During harsh environmental conditions cooperative behaviour increases the chance of individuals surviving |
| Open-Source Code | https://www.comses.net/codebases/4249/releases/1.0.0/ | https://www.openabm.org/model/5287/. | https://doi.org/10.17605/OSF.IO/JM3ZY |
| Programming Language | NetLogo 5.0.5 | NetLogo 5.3 | NetLogo 6.2.2 |