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• W3010770048 abstract "•We introduce a mathematical model of food exchange•We evaluate this model by using historical patterns of crop production•We find that generic networks of exchange can mitigate variability in production•Only a handful of networks of exchange guarantee stable human population sizes Variability in crop production from year to year is a fact of life. One strategy that human societies have used to pool risk is the exchange of food between locations: in effect, a good year at one location might smooth out bad years elsewhere. Here, we introduce a mathematical model of food exchange with two goals. First, we quantify to what extent food exchange can mitigate variability in productivity between different locations. Second, we shed light on whether specific networks of food exchange are more effective than others. To tackle this second question, we show that certain networks lead to stable human population sizes, whereas others lead to instability and depopulation whenever overall crop production becomes low. This second finding has implications for our understanding of exchange and population dynamics in ancestral societies and could also help us in identifying new strategies for tackling future food security. Food exchange between human populations can mitigate the risk arising from variable food production. Networks of exchange vary according to context but tend to fall into a relatively small number of qualitatively different types, including altruism, reciprocity, and resource pooling. This apparent canalization raises the question of whether specific networks of food exchange exhibit features that allow them to persist in the longer term, and we address this question by using a model of food exchange among multiple populations. First, we show that essentially any mode of exchange in our model will help to buffer the risk associated with local environmental variability. However, we also find that only a limited set of networks will guarantee population stability when resources are scarce. These stabilizing networks overlap empirical classifications of exchange, suggesting that population stability could provide an important filter for viable modes of exchange. Food exchange between human populations can mitigate the risk arising from variable food production. Networks of exchange vary according to context but tend to fall into a relatively small number of qualitatively different types, including altruism, reciprocity, and resource pooling. This apparent canalization raises the question of whether specific networks of food exchange exhibit features that allow them to persist in the longer term, and we address this question by using a model of food exchange among multiple populations. First, we show that essentially any mode of exchange in our model will help to buffer the risk associated with local environmental variability. However, we also find that only a limited set of networks will guarantee population stability when resources are scarce. These stabilizing networks overlap empirical classifications of exchange, suggesting that population stability could provide an important filter for viable modes of exchange. For humans relying on agriculture or foraging for caloric intake, uncertainty mediated by the weather is a fact of life. Variability in productivity from year to year has been a constant, identified and often supported via climate reconstruction across multiple ancestral systems,1Burns B.T. Simulated Anasazi storage behavior using crop yields reconstructed from tree rings, AD 652-1968. University of Arizona, 1983Google Scholar, 2Anderson D.G. Stahle D.W. Cleaveland M.K. Paleoclimate and the potential food reserves of Mississippian societies: a case study from the Savannah River Valley.Am. Antiq. 1995; 60: 258-286Crossref Scopus (66) Google Scholar, 3Winterhalder B. Goland C. An evolutionary ecology perspective on diet choice.in: Gremillion K.J. People, Plants, and Landscapes. University of Alabama Press, 1997: 123-160Google Scholar, 4Halstead P. O’Shea J. Bad Year Economics: Cultural Responses to Risk and Uncertainty. Cambridge University Press, 2004Google Scholar, 5Kohler T.A. Johnson C.D. Varien M. Ortman S. Reynolds R. Kobti Z. Cowan J. Kolm K. Smith S. Yap L. Settlement ecodynamics in the prehispanic central Mesa Verde region.in: Kohler T.A. van der Leeuw S.E. The Model-Based Archaeology of Socionatural Systems. School for Advanced Research Press, 2007: 61-104Google Scholar, 6Bocinsky R.K. Kohler T.A. A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest.Nat. Commun. 2014; 5: 5618Crossref PubMed Scopus (54) Google Scholar, 7Bocinsky R.K. Rush J. Kintigh K.W. Kohler T.A. Exploration and exploitation in the macrohistory of the pre-Hispanic Pueblo Southwest.Sci. Adv. 2016; 2: e1501532Crossref PubMed Scopus (41) Google Scholar documented in present-day societies,4Halstead P. O’Shea J. Bad Year Economics: Cultural Responses to Risk and Uncertainty. Cambridge University Press, 2004Google Scholar,8Cohen J.E. Xu M. Schuster W.S. Stochastic multiplicative population growth predicts and interprets Taylor’s power law of fluctuation scaling.Proc. Biol. Sci. 2013; 280: 20122955Crossref PubMed Scopus (38) Google Scholar, 9Döring T.F. Knapp S. Cohen J.E. Taylors power law and the stability of crop yields.Field Crops Res. 2015; 183: 294-302Crossref Scopus (34) Google Scholar, 10Ravallion M. Markets and Famines. Clarendon Press, 1987Google Scholar, 11Ravallion M. Famines and economics.J. Econ. Lit. 1997; 35: 1205-1242Google Scholar and carrying important implications for future food security.12Baethgen W.E. Climate risk management for adaptation to climate variability and change.Crop Sci. 2010; 50: S70-S76Crossref Scopus (25) Google Scholar, 13Lobell D.B. Roberts M.J. Schlenker W. Braun N. Little B.B. Rejesus R.M. Hammer G.L. Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest.Science. 2014; 344: 516-519Crossref PubMed Scopus (491) Google Scholar, 14Wu A. Hammer G.L. Doherty A. von Caemmerer S. Farquhar G.D. Quantifying impacts of enhancing photosynthesis on crop yield.Nat. Plants. 2019; 5: 380-388Crossref PubMed Scopus (81) Google Scholar As one might expect for such an inevitable challenge, there are multiple, established strategies that ancestral human populations have used to mitigate the effects of changing productivity, and these are broadly classified as diversification, storage, mobility, and exchange.4Halstead P. O’Shea J. Bad Year Economics: Cultural Responses to Risk and Uncertainty. Cambridge University Press, 2004Google Scholar,15Lightfoot K.G. Food redistribution among prehistoric pueblo groups.Kiva. 1979; 44: 319-339Crossref Google Scholar,16O’Shea J. Coping with scarcity: exchange and social storage.in: Sheridan A. Bailey G. Economic Archaeology: Towards an Integration of Ecological and Social Approaches. Oxford University Press, 1981: 167-183Google Scholar Diversification here refers to a broadening of the caloric base, e.g., via a range of strains of the same crop planted in the same year with different expected yields and levels of resilience to weather variability.17Herhahn C.L. Hill J.B. Modeling agricultural production strategies in the northern Rio Grande Valley, New Mexico.Hum. Ecol. 1998; 26: 469-487Crossref Scopus (17) Google Scholar,18Bocinsky R.K. Varien M.D. Comparing maize paleoproduction models with experimental data.J. Ethnobiol. 2017; 37: 282-308Crossref Scopus (9) Google Scholar Storage of food from productive periods can allow consumption in times of scarcity but is historically limited by technology.19DeBoer W.R. Subterranean storage and the organization of surplus: the view from eastern North America.Southeast. Archaeol. 1988; 7: 1-20Google Scholar Mobility is the choice to move on from a region experiencing lower productivity and is most likely mediated by the exchange of information for determining new, more productive locations.4Halstead P. O’Shea J. Bad Year Economics: Cultural Responses to Risk and Uncertainty. Cambridge University Press, 2004Google Scholar Finally, the exchange of food among individuals or populations can also mitigate uncertainty.20Sahlins M. Stone Age Economics. Routledge, 1972Google Scholar,21Dillian C.D. White C.L. Introduction: perspectives on trade and exchange.in: Dillian C.D. White C.L. Trade and Exchange. Springer, 2010: 3-14Crossref Scopus (14) Google Scholar If the yield for a particular household or population is at least somewhat uncorrelated with its exchange partners, there is the potential that linking and transferring food between locations can allow variability across locations to smooth variability in productivity over time. In this manuscript we’ll use a mathematical model to evaluate the efficacy and consequences of the last of these categories: food exchange. This strategy is documented across a broad range of human societies on multiple scales,21Dillian C.D. White C.L. Introduction: perspectives on trade and exchange.in: Dillian C.D. White C.L. Trade and Exchange. Springer, 2010: 3-14Crossref Scopus (14) Google Scholar, 22Brumfiel E.M. Earle T.K. Specialization, Exchange and Complex Societies. Cambridge University Press, 1987Google Scholar, 23Hegmon M. Risk reduction and variation in agricultural economies: a computer simulation of hopi agriculture.in: Isaac B. Research in Economic Anthropology. JAI Press, 1989: 89-121Google Scholar, 24Winterhalder B. Diet choice, risk, and food sharing in a stochastic environment.J. Anthropol. Archaeol. 1986; 5: 369-392Crossref Scopus (258) Google Scholar, 25Winterhalder B. Gifts given, gifts taken: the behavioral ecology of nonmarket, intragroup exchange.J. Archaeol. Res. 1997; 5: 121-168Crossref Scopus (59) Google Scholar, 26Winterhalder B. Smith E.A. Analyzing adaptive strategies: human behavioral ecology at twenty-five.Evol. Anthropol. 2000; 9: 51-72Crossref Scopus (358) Google Scholar, 27Gurven M. To give and to give not: the behavioral ecology of human food transfers.Behav. Brain Sci. 2004; 27: 543-559Crossref Google Scholar, 28Aktipis C.A. Cronk L. de Aguiar R. Risk-pooling and herd survival: an agent-based model of a Maasai gift-giving system.Hum. Ecol. 2011; 39: 131-140Crossref Scopus (76) Google Scholar, 29Hao Y. Armbruster D. Cronk L. Aktipis C.A. Need-based transfers on a network: a model of risk-pooling in ecologically volatile environments.Evol. Hum. Behav. 2015; 36: 265-273Crossref Scopus (21) Google Scholar including the kind of longer-range exchange necessary for mitigating local environmental variability. For example, in the case of maize agriculture in Pueblo society in the American Southwest, there is physical evidence establishing that maize could have been exchanged on length scales of up to 100 km30Cordell L.S. Durand S.R. Antweiler R.C. Taylor H.E. Toward linking maize chemistry to archaeological agricultural sites in the North American southwest.J. Archaeol. Sci. 2001; 28: 501-513Crossref Scopus (17) Google Scholar, 31Benson L. Cordell L. Vincent K. Taylor H. Stein J. Farmer G.L. Futa K. Ancient maize from Chacoan great houses: where was it grown?.Proc. Natl. Acad. Sci. USA. 2003; 100: 13111-13115Crossref PubMed Scopus (75) Google Scholar, 32Cordell L.S. Toll H.W. Toll M.S. Windes T.C. Archaeological corn from Pueblo Bonito, Chaco Canyon, New Mexico: dates, contexts, sources.Am. Antiq. 2008; 73: 491-511Crossref Scopus (31) Google Scholar, 33Crabtree S.A. Inferring ancestral pueblo social networks from simulation in the central Mesa Verde.J. Archaeol. Method Theory. 2015; 22: 144-181Crossref Scopus (18) Google Scholar among Pueblo communities, and biophysical estimates (based on energy expenditure for transportation) have also put the maximum length scale over which it would have been plausible to transport maize in this system at around 50 km or greater,15Lightfoot K.G. Food redistribution among prehistoric pueblo groups.Kiva. 1979; 44: 319-339Crossref Google Scholar,34Drennan R.D. Long-distance transport costs in pre-Hispanic Mesoamerica.Am. Anthropol. 1984; 86: 105-112Crossref Scopus (97) Google Scholar broadly consistent with the physical evidence. Finally, variability in yield across space in this region has been reconstructed from tree-ring data and estimated to vary on length scales of tens of kilometers, as well as from year to year.7Bocinsky R.K. Rush J. Kintigh K.W. Kohler T.A. Exploration and exploitation in the macrohistory of the pre-Hispanic Pueblo Southwest.Sci. Adv. 2016; 2: e1501532Crossref PubMed Scopus (41) Google Scholar Beyond this example, evidence for long-distance exchange as a mechanism for mitigating uncertainty has also been documented for other ancestral societies, e.g., in agricultural production in Mississipian culture35Finney F.A. Exchange and risk management in the upper Mississippi River Valley, A.D. 1000-1200.MidCont. J. Archaeol. 2000; 25: 353-376Google Scholar and hunter-gatherer populations bridging mainland California and the Santa Barbara Channel Islands.36Arnold J.E. Complex hunter-gatherer-fishers of prehistoric California: chiefs, specialists, and maritime adaptations of the Channel Islands.Am. Antiq. 1992; 57: 60-84Crossref Scopus (211) Google Scholar These case studies provide evidence that when the conditions for agricultural success are sufficiently variable over space and time, the transfer of food has the potential to mitigate this volatility. But quantifying the precise structure of exchange in any given example—who gives to whom, how much, and why—continues to occupy the attention of anthropologists, economic historians, and human behavioral ecologists. Polanyi,37Polanyi K. The Great Transformation.Volume 2. Beacon Press, 1944Google Scholar Sahlins,20Sahlins M. Stone Age Economics. Routledge, 1972Google Scholar and other authors38Udy S.H. Organization of Work: A Comparative Analysis of Production among Nonindustrial Peoples. Hraf Press, 1959Google Scholar,39Fiske A.P. Structures of Social Life: The Four Elementary Forms of Human Relations: Communal Sharing, Authority Ranking, Equality Matching, Market Pricing. Free Press, 1991Google Scholar developed pioneering classification schemes for the qualitatively different ways that groups exchange food and other commodities at multiple scales. For example, Sahlins aggregated across many case studies to identify three positive modes of exchange: (1) generalized reciprocity, where resources are given without the expectation of a return; (2) balanced reciprocity, where exchange is equal in each direction between a pair of partners; and (3) resource pooling, where resources are given to a central hub and then redistributed. Naturally, no one typology is unlikely to hold across all examples and scales, and the extensive analytical and empirical work continuing in this area points to that diversity.23Hegmon M. Risk reduction and variation in agricultural economies: a computer simulation of hopi agriculture.in: Isaac B. Research in Economic Anthropology. JAI Press, 1989: 89-121Google Scholar,26Winterhalder B. Smith E.A. Analyzing adaptive strategies: human behavioral ecology at twenty-five.Evol. Anthropol. 2000; 9: 51-72Crossref Scopus (358) Google Scholar,27Gurven M. To give and to give not: the behavioral ecology of human food transfers.Behav. Brain Sci. 2004; 27: 543-559Crossref Google Scholar,33Crabtree S.A. Inferring ancestral pueblo social networks from simulation in the central Mesa Verde.J. Archaeol. Method Theory. 2015; 22: 144-181Crossref Scopus (18) Google Scholar,40Halstead P. From reciprocity to redistribution: modelling the exchange of livestock in Neolithic Greece.Anthropozoologica. 1992; 16: 19-30Google Scholar, 41Bird R.L.B. Bird D.W. Delayed reciprocity and tolerated theft: the behavioral ecology of food-sharing strategies.Curr. Anthropol. 1997; 38: 49-78Crossref Google Scholar, 42Downey S.S. Can properties of labor-exchange networks explain the resilience of swidden agriculture?.Ecol. Soc. 2010; 15: 1-17Crossref Scopus (28) Google Scholar, 43Clark J.K. Crabtree S.A. Examining social adaptations in a volatile landscape in northern Mongolia via the agent-based model ger grouper.Land (Basel). 2015; 4: 157-181Google Scholar Still, although there are multiple classification schemes, typically there is significant overlap between these categorizations. For example, Sahlins’s balanced reciprocity closely mirrors Polanyi’s pre-market “reciprocity” and Fiske’s “equality matching.”39Fiske A.P. Structures of Social Life: The Four Elementary Forms of Human Relations: Communal Sharing, Authority Ranking, Equality Matching, Market Pricing. Free Press, 1991Google Scholar,44Fiske A.P. The four elementary forms of sociality: framework for a unified theory of social relations.Psychol. Rev. 1992; 99: 689-723Crossref PubMed Scopus (1339) Google Scholar In addition to the precise categorization, the interpretations of these categories have also been extensively critiqued—e.g., there is evidence disputing the influence of kinship distance on exchange type, one of Sahlins's original arguments for this classification,45Lebra T.S. An alternative approach to reciprocity.Am. Anthropol. 1975; 77: 550-565Crossref Scopus (20) Google Scholar,46Mitchell W.E. The defeat of hierarchy: gambling as exchange in a Sepik society.Am. Ethnol. 1988; 15: 638-657Crossref Scopus (29) Google Scholar and there is debate in general over what we can infer about the human motivations leading to any one particular form of exchange.39Fiske A.P. Structures of Social Life: The Four Elementary Forms of Human Relations: Communal Sharing, Authority Ranking, Equality Matching, Market Pricing. Free Press, 1991Google Scholar However, even if the precise structure of networks of human food exchange (as well as the driving forces that underly it) remains the subject of debate, it seems clear that there are consistent patterns in the ways that humans have exchanged food. This consistency raises a central question: are there specific modes of food exchange that exhibit features allowing them to persist and displace other types of exchange in the long term? One approach to this question is to model the gradual evolution of exchange strategies over time, usually in a constant environment.47Axelrod R. Hamilton W.D. The evolution of cooperation.Science. 1981; 211: 1390-1396Crossref PubMed Scopus (4783) Google Scholar, 48Bowles S. Gintis H. Origins of human cooperation.in: Hammerstein P. Genetic and Cultural Evolution of Cooperation. MIT Press, 2003: 429-443Google Scholar, 49Bowles S. Gintis H. The evolution of strong reciprocity: cooperation in heterogeneous populations.Theor. Popul. Biol. 2004; 65: 17-28Crossref PubMed Scopus (411) Google Scholar, 50Boyd R. Gintis H. Bowles S. Richerson P.J. The evolution of altruistic punishment.Proc. Natl. Acad. Sci. USA. 2003; 100: 3531-3535Crossref PubMed Scopus (984) Google Scholar Evolutionarily stable strategies are those that then tend to win out over time. However, these approaches typically do not address the variability or stability of population dynamics once a given exchange strategy has arisen—and these are particularly important factors in terms of understanding food security and sustainability in a variable environment. Here, we fill this gap by considering both the response of populations to environmental uncertainty and the stability of population equilibria to small perturbations once a given network of exchange has arisen. We develop a range of models based on the consumption and depletion of a natural resource by multiple groups in different locations by adapting existing consumer-resource models from ecology.51MacArthur R. Levins R. The limiting similarity, convergence, and divergence of coexisting species.Am. Nat. 1967; 101: 377-385Crossref Google Scholar, 52Tilman D. Resources: a graphical-mechanistic approach to competition and predation.Am. Nat. 1980; 116: 362-393Crossref Google Scholar, 53Chesson P. Macarthur’s consumer-resource model.Theor. Popul. Biol. 1990; 37: 26-38Crossref Scopus (108) Google Scholar, 54Chesson P. Mechanisms of maintenance of species diversity.Annu. Rev. Ecol. Syst. 2000; 31: 343-366Crossref Scopus (3614) Google Scholar, 55Abrams P.A. Rueffler C. Dinnage R. Competition-similarity relationships and the nonlinearity of competitive effects in consumer-resource systems.Am. Nat. 2008; 172: 463-474Crossref PubMed Scopus (36) Google Scholar, 56Momeni B. Xie L. Shou W. Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions.eLife. 2017; 6: e25051Crossref PubMed Google Scholar, 57Goyal A. Dubinkina V. Maslov S. Multiple stable states in microbial communities explained by the stable marriage problem.ISME J. 2018; 12: 2823-2834Crossref PubMed Scopus (27) Google Scholar Our focus on population stability58Lyapunov A.M. The general problem of the stability of motion.Int. J. Control. 1892; 55: 531-534Crossref Scopus (553) Google Scholar then draws from and mirrors extensive work on stability analyses in both ecological communities59May R.M. Will a large complex system be stable?.Nature. 1972; 238: 413-414Crossref PubMed Scopus (1480) Google Scholar, 60Holling C.S. Resilience and stability of ecological systems.Annu. Rev. Ecol. Syst. 1973; 4: 1-23Crossref Google Scholar, 61Goh B. Stability in models of mutualism.Am. Nat. 1979; 113: 261-275Crossref Google Scholar, 62Grimm V. Schmidt E. Wissel C. On the application of stability concepts in ecology.Ecol. Modell. 1992; 63: 143-161Crossref Scopus (96) Google Scholar, 63Allesina S. Tang S. Stability criteria for complex ecosystems.Nature. 2012; 483: 205-208Crossref PubMed Scopus (519) Google Scholar, 64Coyte K.Z. Schluter J. Foster K.R. The ecology of the microbiome: networks, competition, and stability.Science. 2015; 350: 663-666Crossref PubMed Scopus (621) Google Scholar, 65Butler S. O’Dwyer J.P. Stability criteria for complex microbial communities.Nat. Commun. 2018; 9: 2970Crossref PubMed Scopus (36) Google Scholar, 66Butler S. O’Dwyer J.P. Cooperation and stability for complex systems in resource-limited environments.Theor. Ecol. 2020; 50https://doi.org/10.1007/s12080-019-00447-5Crossref Scopus (5) Google Scholar and market-based commodity exchange.67Samuelson P.A. The stability of equilibrium: comparative statics and dynamics.Econometrica. 1941; 9: 97-120Crossref Google Scholar, 68Arrow K.J. Hurwicz L. On the stability of the competitive equilibrium, i.Econometrica. 1958; 26: 522-552Crossref Google Scholar, 69Scarf H. Some examples of global instability of the competitive equilibrium.Int. Econ. Rev. 1960; 1: 157-172Crossref Google Scholar, 70Negishi T. The stability of a competitive economy: a survey article.Econometrica. 1962; 30: 635-669Crossref Google Scholar, 71Fisher F.M. The stability of general equilibrium: results and problems.in: Artis M.J. Nobay A.R. Essays in Economic Analysis: The Proceedings of the Association of University Teachers of Economics Sheffield 1975. Cambridge University Press, 1976: 3-29Google Scholar, 72Deaton A. Laroque G. On the behaviour of commodity prices.Rev. Econ. Stud. 1992; 59: 1-23Crossref Scopus (544) Google Scholar Here, we apply this new approach, and we first show that any network of exchange can buffer a range of types of environmental variation as long as this variation is uncorrelated across locations. Importantly, this spreading of risk doesn’t just reduce the variation in the total population size, summed across all sites;73Yachi S. Loreau M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis.Proc. Natl. Acad. Sci. USA. 1999; 96: 1463-1468Crossref PubMed Scopus (1620) Google Scholar it also guarantees reduced population variation relative to mean population sizes at each individual site. The robustness of this mathematical result bears out the intuition that food exchange can buffer environmental variability when some locations are doing well and others are less productive. On the other hand, we can’t expect exchange to buffer variability in cases of system-wide reductions in productivity, e.g., during an extended drought.74Tainter J. The Collapse of Complex Societies. Cambridge University Press, 1990Google Scholar, 75Clare L. Weninger B. Social and biophysical vulnerability of prehistoric societies to rapid climate change.Documenta Praehistorica. 2010; 37: 283-292Crossref Scopus (21) Google Scholar, 76Brown J.H. Burger J.R. Burnside W.R. Chang M. Davidson A.D. Fristoe T.S. Hamilton M.J. Hammond S.T. Kodric-Brown A. Mercado-Silva N. et al.Macroecology meets macroeconomics: Resource scarcity and global sustainability.Ecol. Eng. 2014; 65: 24-32Crossref Scopus (31) Google Scholar During this kind of event, we’d most likely expect all population sizes to be reduced. However, our analysis of population stability demonstrates that the outcome of system-wide scarcity can be even worse than reduced population sizes: for sufficiently low productivity, we show that generic modes of exchange will lead to instability and potentially complete depopulation at one or more locations. We find that only a handful of networks, which we term stabilizing modes of exchange, guarantee stable population sizes even during scarcity. One surprising outcome is that these special, stabilizing modes of exchange heavily overlap Sahlins’s (and others’) categories of exchange. This suggests that population stability could be an important filter for any given system of food exchange to pass through in order to persist long term and that networks of exchange that don’t guarantee stability could ultimately lead to depopulation or system-wide collapse.60Holling C.S. Resilience and stability of ecological systems.Annu. Rev. Ecol. Syst. 1973; 4: 1-23Crossref Google Scholar,74Tainter J. The Collapse of Complex Societies. Cambridge University Press, 1990Google Scholar Putting the evidence for food exchange in ancestral societies together with the lack of evidence about its specifics, we now set out some guidelines and criteria for our modeling framework. First, we need to quantify more clearly both what is meant by exchange and how we will quantify the impact of environmental variability on human populations. Exchange is a term that has been used to describe a broad set of distinct processes, ranging from food sharing within a household to pooling the proceeds of a hunt to external trade between individuals or groups in distinct geographic locations. In this article, we will model exchange over relatively long distances between populations farming crops, and a critical feature of our models will be that population sizes are explicitly coupled to resource dynamics as a result of the effects of resource availability and consumption on population growth. We will therefore quantify the potential for exchange to mitigate environmental variability in terms of a reduction in the response of population sizes (at one or multiple locations) to environmental variability over time. In other words, we’ll evaluate the success of food exchange as a strategy in terms of how well it smooths out the environment that a given population experiences. Second, there is a large degree of uncertainty in how to model the mathematical form of exchange in ancestral societies. We’ll therefore seek to identify model outcomes that are robust across a range of functional forms for the exchange process. We’ll also leave the network of which locations are connected via exchange as unspecified—so that we can probe the impact of the structure of this network on model outcomes. Still, although there are significant unknowns in how to model this process, there are clues. For example, there is strong evidence that crop failure was a regular occurrence in the Pueblo Southwest, one of our motivating examples given in the Introduction—suggesting that, at least in some societies, a degree of consistency and organization would be plausible.7Bocinsky R.K. Rush J. Kintigh K.W. Kohler T.A. Exploration and exploitation in the macrohistory of the pre-Hispanic Pueblo Southwest.Sci. Adv. 2016; 2: e1501532Crossref PubMed Scopus (41) Google Scholar,15Lightfoot K.G. Food redistribution among prehistoric pueblo groups.Kiva. 1979; 44: 319-339Crossref Google Scholar It has also been proposed that the transfer of food would have been in proportion to surplus,15Lightfoot K.G. Food redistribution among prehistoric pueblo groups.Kiva. 1979; 44: 319-339Crossref Google Scholar,77Kohler T.A. Van West C.R. The calculus of self-interest in the development of cooperation: sociopolitical development and risk among the northern Anasazi.in: Tainter J.A. Evolving Complexity and Environmental Risk in the Prehistoric Southwest. CRC Press, 1996: 169-196Google Scholar giving some hints to the functional dependence of exchange on resource availability. Summarizing, we will look for robust results while motivating any assumptions by using what we do know about exchange in ancestral societies. Third and finally, our model will focus on the material benefits of food exchange, although we acknowledge that any analysis of the process of exchange also naturally raises questions for broader social phenomena, continuing a long history of studying the intertwining of economics with the social and political contexts of exchange.78Earle T.K. Prehistoric economics and the archaeology of exchange.in: Ericson J.E. Earle T.K. Contexts for Prehistoric Exchange. Elsevier, 1982: 1-12Crossref Google Scholar For example, food exchange in the Southwest might plausibly have aided survival while simultaneously feeding off and building social ties.33Crabtree S.A. Inferring ancestral pueblo social networks fro" @default.
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• W3010770048 title "Stability Constrains How Populations Spread Risk in a Model of Food Exchange" @default.
• W3010770048 cites W1265495117 @default.
• W3010770048 cites W1493042627 @default.
• W3010770048 cites W164628575 @default.
• W3010770048 cites W1966397236 @default.
• W3010770048 cites W1986706966 @default.
• W3010770048 cites W1988936934 @default.
• W3010770048 cites W1989338016 @default.
• W3010770048 cites W1991849763 @default.
• W3010770048 cites W1993365370 @default.
• W3010770048 cites W1994507967 @default.
• W3010770048 cites W1995815484 @default.
• W3010770048 cites W1999320979 @default.
• W3010770048 cites W2010707998 @default.
• W3010770048 cites W2011303397 @default.
• W3010770048 cites W2011702442 @default.
• W3010770048 cites W2011866841 @default.
• W3010770048 cites W2012283013 @default.
• W3010770048 cites W2012902581 @default.
• W3010770048 cites W2015519168 @default.
• W3010770048 cites W2018588595 @default.
• W3010770048 cites W2030430287 @default.
• W3010770048 cites W2031834748 @default.
• W3010770048 cites W2032449684 @default.
• W3010770048 cites W2032601524 @default.
• W3010770048 cites W2043153600 @default.
• W3010770048 cites W2047681048 @default.
• W3010770048 cites W2049546703 @default.
• W3010770048 cites W2052997047 @default.
• W3010770048 cites W2055265134 @default.
• W3010770048 cites W2057728075 @default.
• W3010770048 cites W2061666069 @default.
• W3010770048 cites W2062663664 @default.
• W3010770048 cites W2069994241 @default.
• W3010770048 cites W2071729843 @default.
• W3010770048 cites W2078686441 @default.
• W3010770048 cites W2079858230 @default.
• W3010770048 cites W2082918692 @default.
• W3010770048 cites W2083628088 @default.
• W3010770048 cites W2084496451 @default.
• W3010770048 cites W2086714066 @default.
• W3010770048 cites W2095029930 @default.
• W3010770048 cites W2107745539 @default.
• W3010770048 cites W2110688506 @default.
• W3010770048 cites W2114527142 @default.
• W3010770048 cites W2115745928 @default.
• W3010770048 cites W2122226023 @default.
• W3010770048 cites W2123923825 @default.
• W3010770048 cites W2126452774 @default.
• W3010770048 cites W2128976025 @default.
• W3010770048 cites W2144150776 @default.
• W3010770048 cites W2146969372 @default.
• W3010770048 cites W2148805899 @default.
• W3010770048 cites W2150597302 @default.
• W3010770048 cites W2155753204 @default.
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