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Using Multiplex Networks in the Study of Human Behavioral Evolution.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using Multiplex Networks in the Study of Human Behavioral Evolution./
作者:	Atkisson, Curtis James.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	91 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
Contained By:	Dissertations Abstracts International81-12B.
標題:	Social research. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27671511
ISBN:	9798645485092

Using Multiplex Networks in the Study of Human Behavioral Evolution.
Atkisson, Curtis James.

Using Multiplex Networks in the Study of Human Behavioral Evolution. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 91 p.

Source: Dissertations Abstracts International, Volume: 81-12, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2020.

This item is not available from ProQuest Dissertations & Theses.

This dissertation explores how the complexity of human sociality may have evolved using multilayer networks. Humans are intensely social creatures that rely on each other for assistance and for whom social interactions are the most salient parts of their socioecology. This means that human social interactions will be the majority of what drives our evolution and behavior. Social network analysis developed as a field to measure the impact of social relationships on outcomes of interest. In addition to maintaining relationships with many individuals, humans also interact across many different domains. The standard social network analysis is not able to fully measure or explore the impact of having relationships that span across many domains.The nascent field of multilayer network analysis has been developed to extend methods and insights from single layer social network analysis into a world of multiple layers. Layers can be anything (different modalities in a transportation network, different types of interactions on Twitter, etc.) including different domains of interaction, such as from whom someone gets sugar, with whom someone goes to church, and with whom someone drinks. Network scientists have begun to generalize concepts of single layer social network analysis such as centralities to the multilayer case, helping us quantify and measure these more complex graphs in order to understand social relationships.Merely generalizing existing methods, however, is insufficient because multilayer networks have structural characteristics beyond what is present in the single layer case. To explore the implications of these multilayer characteristics on models of human behavior, chapter 1 looks at multilayer structuring processes. These are dynamics that make relationships between some nodes more likely on some domains than might otherwise be the case. Such processes can prevent individuals from rearranging their networks to make each layer optimal. Not only can understanding these processes help us better understand behavior but failing to incorporate these processes into our analyses can prevent us from detecting the true effect of relationships on outcomes.Ideas using multilayer networks were tested empirically in chapters 2 and 3, amongst the Makushi in the North Rupununi of southern Guyana. This area is relatively culturally homogenous, with few demographic differences between communities. However, around 15 years ago, a road was built that resulted in some communities getting greater exposure to wage and migrant labor than others. Individuals in communities with increased exposure to these forces have needed to reorganize their networks in response. This natural variation in reorganization of networks makes this an appropriate place to test how insights and tools generated from multilayer networks and multilayer network analysis can help us understand human sociality.Chapter 2 develops unique multilayer network measures and statistically tests the effect of those measures relative to measures that are not derived from the multilayer network to see their impact on a domain of interest. Specialization is a hallmark of the human species. Our requirements for a diversity of micro- and macro-nutrients and a constant supply of a large amount of energy in our diets means that specialization requires trade with other specialists or specialists will not have the diversity and density needed in their diets. In the domain of food production, this means that people should produce an over-abundance of a small set of resources and trade it with others for a larger set of resources. This chapter shows that the patterning of trade across partners, specifically having multiple partners with the same relationship type in a food sharing multilayer network (e.g., give meat and receive cassava and rice), increases an individual's food security (the number of days per week they skip a meal). This result shows that having redundant trade relationships leads to increased food security while only having a small number of important partners decreases food security. Chapter 3 tests predictions that would not be possible without appreciating that people exist in multilayer networks. This chapter modernizes the ideas of the sociologist Durkheim by predicting that people who, and people in communities composed of people who, do many of their activities with the same set of people (i.e., those who have high overlap in a multilayer network) will cooperate intensively with that set of people while cooperating little in anonymous interactions. The reverse will be true for people who have many unique partners across their domains of interaction. In effect, people who have or are in societies with high overlap have partners that are very important to them. These important partners need to be maintained or else someone could lose a cooperating partner in many domains. As such, these people will pay the cost of maintaining those relationships in the form of intensive cooperative events (e.g., building a house). People who have or are in societies with low overlap, on the other hand, have greater uncertainty in any of their existing network connections. As such, these people will be willing to pay more to signal to potential partners in order to attract those partners. This chapter shows that having a greater proportion of one's connections be with people who are in the multilayer network more than once (overlap) and being in a community with on average higher overlap leads to more cooperation in intensive cooperative events and less prosocial behavior in anonymous economic games. This result shows that having overlap with individuals across many domains of interaction can lead to increased cooperation in intensive events, even though these events are costly to the helping individual. Together, these results help us better understand part of what underlies the maintenance of social relationships in this particular population, and more generally how the patterning of different types of interactions with multiple partners may be associated with exogenous factors such as the process of food acquisition and production, and the availability of economic activities outside of the community. In Makushi society, it is important for people to have connections with a partner across multiple domains and multiple partners who share that pattern of connections, at least for food security. People who have relationships with connections across multiple domains are motivated to keep them by investing in high-cost, intensive cooperative events, while people who do not have connections such as that are motivated to develop them by recruiting new network partners. In all, this shows that while it is beneficial for a Makushi person to have partners who are important to them, it is detrimental, for a given number of partners, to have only a small number of such important partners. Mechanisms such as these can help us understand why people behave in certain ways with certain network partners. This can help us understand changing patterns of connections as isolated communities become more linked to the outside world and as communities are reorganized due to major demographic shifts.

ISBN: 9798645485092Subjects--Topical Terms:

2122687
Social research.
Subjects--Index Terms:

Anthropology

Using Multiplex Networks in the Study of Human Behavioral Evolution.
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245 1 0 $a Using Multiplex Networks in the Study of Human Behavioral Evolution.
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300 $a 91 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
500 $a Advisor: Borgerhoff Mulder, Monique.
502 $a Thesis (Ph.D.)--University of California, Davis, 2020.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation explores how the complexity of human sociality may have evolved using multilayer networks. Humans are intensely social creatures that rely on each other for assistance and for whom social interactions are the most salient parts of their socioecology. This means that human social interactions will be the majority of what drives our evolution and behavior. Social network analysis developed as a field to measure the impact of social relationships on outcomes of interest. In addition to maintaining relationships with many individuals, humans also interact across many different domains. The standard social network analysis is not able to fully measure or explore the impact of having relationships that span across many domains.The nascent field of multilayer network analysis has been developed to extend methods and insights from single layer social network analysis into a world of multiple layers. Layers can be anything (different modalities in a transportation network, different types of interactions on Twitter, etc.) including different domains of interaction, such as from whom someone gets sugar, with whom someone goes to church, and with whom someone drinks. Network scientists have begun to generalize concepts of single layer social network analysis such as centralities to the multilayer case, helping us quantify and measure these more complex graphs in order to understand social relationships.Merely generalizing existing methods, however, is insufficient because multilayer networks have structural characteristics beyond what is present in the single layer case. To explore the implications of these multilayer characteristics on models of human behavior, chapter 1 looks at multilayer structuring processes. These are dynamics that make relationships between some nodes more likely on some domains than might otherwise be the case. Such processes can prevent individuals from rearranging their networks to make each layer optimal. Not only can understanding these processes help us better understand behavior but failing to incorporate these processes into our analyses can prevent us from detecting the true effect of relationships on outcomes.Ideas using multilayer networks were tested empirically in chapters 2 and 3, amongst the Makushi in the North Rupununi of southern Guyana. This area is relatively culturally homogenous, with few demographic differences between communities. However, around 15 years ago, a road was built that resulted in some communities getting greater exposure to wage and migrant labor than others. Individuals in communities with increased exposure to these forces have needed to reorganize their networks in response. This natural variation in reorganization of networks makes this an appropriate place to test how insights and tools generated from multilayer networks and multilayer network analysis can help us understand human sociality.Chapter 2 develops unique multilayer network measures and statistically tests the effect of those measures relative to measures that are not derived from the multilayer network to see their impact on a domain of interest. Specialization is a hallmark of the human species. Our requirements for a diversity of micro- and macro-nutrients and a constant supply of a large amount of energy in our diets means that specialization requires trade with other specialists or specialists will not have the diversity and density needed in their diets. In the domain of food production, this means that people should produce an over-abundance of a small set of resources and trade it with others for a larger set of resources. This chapter shows that the patterning of trade across partners, specifically having multiple partners with the same relationship type in a food sharing multilayer network (e.g., give meat and receive cassava and rice), increases an individual's food security (the number of days per week they skip a meal). This result shows that having redundant trade relationships leads to increased food security while only having a small number of important partners decreases food security. Chapter 3 tests predictions that would not be possible without appreciating that people exist in multilayer networks. This chapter modernizes the ideas of the sociologist Durkheim by predicting that people who, and people in communities composed of people who, do many of their activities with the same set of people (i.e., those who have high overlap in a multilayer network) will cooperate intensively with that set of people while cooperating little in anonymous interactions. The reverse will be true for people who have many unique partners across their domains of interaction. In effect, people who have or are in societies with high overlap have partners that are very important to them. These important partners need to be maintained or else someone could lose a cooperating partner in many domains. As such, these people will pay the cost of maintaining those relationships in the form of intensive cooperative events (e.g., building a house). People who have or are in societies with low overlap, on the other hand, have greater uncertainty in any of their existing network connections. As such, these people will be willing to pay more to signal to potential partners in order to attract those partners. This chapter shows that having a greater proportion of one's connections be with people who are in the multilayer network more than once (overlap) and being in a community with on average higher overlap leads to more cooperation in intensive cooperative events and less prosocial behavior in anonymous economic games. This result shows that having overlap with individuals across many domains of interaction can lead to increased cooperation in intensive events, even though these events are costly to the helping individual. Together, these results help us better understand part of what underlies the maintenance of social relationships in this particular population, and more generally how the patterning of different types of interactions with multiple partners may be associated with exogenous factors such as the process of food acquisition and production, and the availability of economic activities outside of the community. In Makushi society, it is important for people to have connections with a partner across multiple domains and multiple partners who share that pattern of connections, at least for food security. People who have relationships with connections across multiple domains are motivated to keep them by investing in high-cost, intensive cooperative events, while people who do not have connections such as that are motivated to develop them by recruiting new network partners. In all, this shows that while it is beneficial for a Makushi person to have partners who are important to them, it is detrimental, for a given number of partners, to have only a small number of such important partners. Mechanisms such as these can help us understand why people behave in certain ways with certain network partners. This can help us understand changing patterns of connections as isolated communities become more linked to the outside world and as communities are reorganized due to major demographic shifts.
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653 $a Anthropology
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