5 min read•Last Updated on July 30, 2024
Network games model strategic interactions between agents in a network, where decisions and payoffs depend on connections. This framework helps analyze how network structure impacts outcomes, equilibria, and collective behavior in various social and economic systems.
Game theory provides tools to study network formation, evolution, and information diffusion. By examining link creation, dynamic processes, and influence propagation, we can understand the emergence of social norms, adoption of innovations, and opinion dynamics in networked environments.
Making the complex less complicated: An introduction to social network analysis – MASHe View original
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Frontiers | Public Goods Games on Coevolving Social Network Models View original
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Tools for Transparency: A How-to Guide for Social Network Analysis with NodeXL : Sunlight Foundation View original
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Making the complex less complicated: An introduction to social network analysis – MASHe View original
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Frontiers | Public Goods Games on Coevolving Social Network Models View original
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Making the complex less complicated: An introduction to social network analysis – MASHe View original
Is this image relevant?
Frontiers | Public Goods Games on Coevolving Social Network Models View original
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Tools for Transparency: A How-to Guide for Social Network Analysis with NodeXL : Sunlight Foundation View original
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Making the complex less complicated: An introduction to social network analysis – MASHe View original
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Frontiers | Public Goods Games on Coevolving Social Network Models View original
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Albert-László Barabási is a prominent physicist and researcher known for his work in network theory, particularly regarding the structure and dynamics of complex networks. His pioneering research has greatly influenced social network analysis and network games by introducing concepts such as scale-free networks, which describe how some networks have a few highly connected nodes while most have relatively few connections.
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Albert-László Barabási is a prominent physicist and researcher known for his work in network theory, particularly regarding the structure and dynamics of complex networks. His pioneering research has greatly influenced social network analysis and network games by introducing concepts such as scale-free networks, which describe how some networks have a few highly connected nodes while most have relatively few connections.
Term 1 of 22
Albert-László Barabási is a prominent physicist and researcher known for his work in network theory, particularly regarding the structure and dynamics of complex networks. His pioneering research has greatly influenced social network analysis and network games by introducing concepts such as scale-free networks, which describe how some networks have a few highly connected nodes while most have relatively few connections.
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Information diffusion refers to the process through which information spreads across a network, affecting individuals' knowledge and behavior. This concept is crucial in understanding how ideas, trends, or innovations are communicated and adopted within social networks, as well as the dynamics of influence and decision-making among interconnected agents.
Network Topology: The arrangement of various elements (nodes and connections) in a network, which can significantly influence how information spreads.
Social Influence: The effect that the presence or actions of others in a network have on an individual's behavior or beliefs, often driving the adoption of new information.
Viral Marketing: A marketing strategy that relies on social networks to spread information about a product or service rapidly, similar to the way a virus spreads.
Nash equilibrium is a concept in game theory where no player can benefit from changing their strategy while the other players keep theirs unchanged. This situation arises when each player's strategy is optimal given the strategies of all other players, leading to a stable state in strategic interactions.
Dominant strategy: A strategy that is the best choice for a player, regardless of what the other players do.
Payoff matrix: A table that illustrates the payoffs for each player based on the strategies chosen in a game.
Subgame perfect equilibrium: An extension of Nash equilibrium that applies to extensive form games, ensuring that players' strategies constitute a Nash equilibrium in every subgame.
Pairwise stability refers to a concept in game theory and network analysis where a network or social structure is considered stable if no two agents can improve their individual outcomes by deviating from the current arrangement. This idea is crucial in understanding how individuals in a network form and maintain relationships, leading to insights about the dynamics of social networks and strategic interactions.
Nash Equilibrium: A situation in game theory where no player can gain an advantage by changing their strategy while the other players keep theirs unchanged.
Graph Theory: A branch of mathematics that studies the properties and applications of graphs, which are used to model pairwise relationships between objects.
Coalition Formation: The process through which individuals or groups come together to form a coalition, often to improve their collective outcomes in a strategic setting.
Stochastic stability refers to the resilience of a particular equilibrium in a dynamic system when subjected to random perturbations or noise. It focuses on how likely certain strategies or behaviors are to persist over time in environments where players face uncertainty and adapt their choices based on past experiences. This concept is particularly important in understanding how networks evolve and how boundedly rational agents learn and adjust their strategies in interactive settings.
Equilibrium: A state in which all players in a game have chosen strategies that result in no player having an incentive to deviate from their chosen strategy.
Adaptive Learning: A process by which players modify their strategies over time based on past outcomes and experiences, helping them improve their decision-making in uncertain environments.
Perturbation: A small change or disturbance in a system that can affect the behavior of the players and the outcome of the game.
The price of anarchy refers to the cost incurred by self-interested agents in a network when they act according to their own interests rather than cooperating for a collectively optimal outcome. This concept highlights how individual decision-making can lead to inefficient results, especially in network games and social networks where players' actions can impact others. It underscores the potential gap between the Nash equilibrium, where no player benefits from changing their strategy unilaterally, and the socially optimal outcome.
Nash Equilibrium: A situation in a game where no player can benefit from changing their strategy while the other players keep theirs unchanged.
Selfish Routing: A scenario in network games where users choose their paths based solely on personal benefit, often leading to congestion and suboptimal resource use.
Social Welfare: The overall well-being or utility of all individuals in a society, typically used as a benchmark for evaluating the efficiency of different outcomes.
The price of stability refers to the cost incurred by players in a network game to reach a stable equilibrium that is not the most efficient outcome. This concept highlights how individuals or groups may have to sacrifice their own optimal strategies to achieve a collective equilibrium that is less than ideal, emphasizing the trade-offs involved in decision-making within social networks. It connects with how social dynamics and interactions among players influence their choices and the overall performance of the network.
Nash Equilibrium: A situation in a game where no player can benefit by changing their strategy while the other players keep theirs unchanged.
Coordination Game: A type of game in which all players benefit from making the same choices and must work together to achieve a better outcome.
Social Welfare: The overall well-being of society in terms of economic efficiency and equitable distribution of resources among individuals.
Degree distribution refers to the statistical distribution of the degrees (the number of connections) of the nodes in a network. It provides insight into how interconnected the nodes are and helps in understanding the overall structure and dynamics of networks, such as social networks or communication networks, revealing patterns like whether most nodes have few connections or if some nodes are highly connected.
Network Topology: The arrangement or layout of different elements (links, nodes) in a network, which influences how data flows and how resilient the network is to failures.
Scale-Free Network: A type of network characterized by a power-law degree distribution, meaning a few nodes (hubs) have a very high degree, while many nodes have a low degree.
Node Centrality: A measure of the relative importance of a node within a network, often determined by its degree or other factors like closeness and betweenness.
Centrality is a measure used in network analysis to determine the importance or influence of a node within a network. It reflects how well connected a node is to other nodes, indicating its potential power or role in spreading information or resources across the network. In various contexts, different centrality measures can highlight unique aspects of a node's influence, leading to insights about social structures and strategic interactions.
Degree Centrality: A specific measure of centrality that counts the number of direct connections a node has, indicating its immediate influence within the network.
Betweenness Centrality: A measure that calculates how often a node acts as a bridge along the shortest path between two other nodes, highlighting its role in facilitating communication and control.
Closeness Centrality: This metric determines how quickly a node can access other nodes in the network, based on its average distance to all other nodes, suggesting its efficiency in communication.
Betweenness centrality is a measure of a node's importance in a network, calculated based on the number of shortest paths that pass through it. This concept highlights how much a particular node acts as a bridge between other nodes, influencing the flow of information or resources across the network. Nodes with high betweenness centrality are critical for connecting disparate parts of the network and can significantly impact communication and collaboration within social networks.
closeness centrality: A measure of how close a node is to all other nodes in the network, based on the shortest paths from that node to others.
degree centrality: The simplest measure of centrality, representing the number of direct connections a node has within the network.
network connectivity: A concept that describes how well nodes in a network are connected to each other, influencing the overall efficiency and robustness of the network.
Community structure refers to the organization of nodes within a network, where nodes are often grouped based on their connectivity and relationships with one another. This concept is vital for understanding how social networks are formed, as it provides insights into the interactions and behaviors of individuals or entities within a specific context. Analyzing community structures helps identify patterns, such as how tightly-knit groups operate and influence larger network dynamics.
Network Centrality: A measure of the importance of a node in a network, often assessed through various metrics like degree centrality, closeness centrality, and betweenness centrality.
Clustering Coefficient: A measure that quantifies the degree to which nodes in a graph tend to cluster together, indicating the likelihood that two neighbors of a node are also connected.
Modularity: A measure used to quantify the structure of a network by determining the strength of division of a network into modules or communities.
The independent cascade model is a framework used to describe the spread of influence or information through a network, where each node has the potential to activate its neighbors independently. In this model, once a node becomes active, it has a fixed probability of activating each of its inactive neighbors in the next time step, creating a cascading effect throughout the network. This model is particularly relevant for understanding how behaviors, innovations, or rumors can propagate within social networks.
Network Topology: The arrangement and interconnections of nodes in a network, which can significantly influence the dynamics of information spread.
Influence Maximization: The process of identifying the most effective nodes in a network to target for activation in order to maximize the spread of influence.
Epidemic Threshold: The critical point in a network where the number of initial active nodes leads to widespread propagation of an influence or infection throughout the network.
The linear threshold model is a framework used to understand how individuals in a network make decisions based on their interactions with others. In this model, each individual has a threshold, which represents the minimum proportion of their neighbors that need to adopt a certain behavior before they themselves will also adopt it. This concept is crucial for analyzing how behaviors and opinions spread through social networks and can be applied to various scenarios like marketing strategies and social influence.
Influence Maximization: The process of identifying the most influential nodes in a network to maximize the spread of information or behaviors.
Cascade Model: A model that describes how a small initial adoption of a behavior can lead to a large-scale spread through a network, similar to a domino effect.
Social Network Analysis: A method used to study the relationships and structures within social networks, helping to understand how social connections affect behaviors and interactions.
Social influence is the process by which individuals change their thoughts, feelings, or behaviors as a result of real or imagined pressure from others. It highlights how the presence and actions of others can impact individual choices and societal norms, shaping group dynamics and interactions within a network.
Peer Pressure: The influence exerted by a peer group encouraging individuals to change their attitudes, values, or behaviors to conform to group norms.
Conformity: The act of matching attitudes, beliefs, and behaviors to group norms, often driven by the desire for acceptance or avoidance of conflict.
Social Norms: The unwritten rules and expectations regarding behavior within a social group that guide individuals in their interactions.
Information cascades occur when individuals, based on the actions or decisions of those before them, make choices without considering their private information. This can lead to a situation where early decisions disproportionately influence the behavior of later individuals, creating a domino effect. In network games and social network analysis, understanding information cascades is crucial as they illustrate how information spreads through networks and affect decision-making processes.
Herd Behavior: A phenomenon where individuals in a group follow the actions of others rather than relying on their own knowledge or analysis.
Social Proof: A psychological phenomenon where people assume the actions of others in an attempt to reflect correct behavior in a given situation.
Network Effects: The phenomenon whereby increased numbers of participants improve the value of a product or service, often influenced by information cascades.
Herding behavior refers to the phenomenon where individuals in a group make decisions based on the actions of others, often leading to a collective movement or trend. This behavior is driven by social influence, where people conform to the perceived choices of others rather than relying solely on their own information or analysis. It can significantly impact market dynamics and social networks, causing rapid shifts in opinion or behavior.
Social influence: The effect that the actions and opinions of others have on an individual's own behaviors and decisions.
Network effects: A situation where the value of a product or service increases as more people use it, often reinforcing herding behavior.
Fads: Short-lived trends or interests that gain popularity rapidly within a group, often due to herding behavior.