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17 results for “Reputations”
A Reputation Game Simulation: Emergent Social Phenomena from Information Theory
<p>Here, the data underlying the article "A Reputation Game Simulation: Emergent Social Phenomena from Information Theory" (<a href="https://doi.org/10.1002/andp.202100277">https://doi.org/10.1002/andp.202100277</a>) is provided.<br> <br> The data is structured according to the figures it has been used for. There are</p> <ul> <li>example simulations with basic communication strategies in the folder "single_simulations_3_agents" (Figures 4,5,8,D1)</li> <li>statistical simulations with 3 agents and special communication strategies in the folder "statistical_simulations_3_agents" (Figures 9-13, the upper panel of figure 15, figures 16-18, D2 and the left panels of figure D3)</li> <li>statistical simulations with 4 agents and special communication strategies in the folder "statistical_simulations_4_agents" (Figure 14, the middle panel of figure 15, the middle panels of figure D3 and the upper panels of figures D4, D5)</li> <li>statistical simulations with 5 agents and special communication strategies in the folder "statistical_simulations_5_agents" (The lower panel of figure 15, the right panels of figure D3 and the lower panels of figures D4,D5)</li> <li>propaganda simulations in the folder "propaganda_simulations" (Figure 7)</li> </ul> <p><br> Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Reputation Communication from an Information Perspective
<p>Here, the data underlying the article "Reputation Communication from an Information Perspective" is provided.<br> <br> There are two example simulations, one with 3 ordinary agents and one with a dominant agent among two ordinary agents. Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Information and Agreement in the Reputation Game Simulation
<p>Here, the data underlying the article "Information and Agreement in the Reputation Game Simulation" is provided.<br> <br> There are 100 simulations with different random seeds to ensure statistically meaningful results. Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>first line <ul> <li>x_est: intrinsic honesties of the agents</li> <li>fr_affinities: intrinsic friendship affinity values of the agents</li> <li>shynesses: intrinsic shyness values of the agents</li> <li>perc_one_to_one: percentage of one-to-one conversations</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> <li>mode: strategy used by the special agent</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b_set: set of receivers. Can be either a single receiver or several</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>relationsc_<id>: number of conversations the updating agent has had with agent <id></li> <li>relationsm_<id>: number of messages the updating agent received about agent <id></li> <li>friendship+_to_<id>: the updating agent counts one friendly statement of agent <id>, i.e. the updating agent rates agent <id> now a little more as a friend</li> <li>friendship-_to_<id>: the updating agent counts one unfriendly statement of agent <id>, i.e. the updating agent rates agent <id> now a little more as an enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x/fr_affinity/shyness: the agent's intrinsic personality traits</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>relationsm/relationsc: number of messages (conversations) the agent heard about (had with) all others</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friendships: the agent's friendship status with all others, given as parameters of a beta function</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Figure 8. Bivariate plots for reputed T in A taxonomic revision of the Tragulus mouse-deer (Artiodactyla)
Figure 8. Bivariate plots for reputed T. napu specimens from the small Sundaland islands.
Dataset Questionnaire Driving Repeat Purchases and E-WOM: How Price, Reputation, Hedonic Appeal, and Social Interaction Shape Consumer Behavior in Indonesia's E-Commerce Smartphone Market
<p>The following dataset is a dataset from a study that investigated price advantage, reputation, hedonic effort, and social interaction influence customer satisfaction, which in turn impacts repurchase intention and e-WOM (electronic word-of-mouth).</p>
Is reputation on Stack Overflow always a good indicator for users' expertise? No!
<p>Dataset for RQ1 and RQ2. </p>
Figure 8. Bivariate plots for reputed T in A taxonomic revision of the Tragulus mouse-deer (Artiodactyla)
Figure 8. Bivariate plots for reputed T. napu specimens from the small Sundaland islands.
Replication package for: Shirking with Good Reputation? Evidence from Hotel Industry (Version 2)
<p>This replication package contains the data and the code to generate the paper’s main results, as well as the Online Appendix, for “Shirking with Good Reputation? Evidence from Hotel Industry” by Fang-Chang Kuo.</p>
HCPN Model for the article A Self-Sovereign Identity-based Authentication and Reputation Protocol for IoV Applications
<p>HCPN modules of the entire formal validation model of the article A Self-Sovereign Identity-based Authentication and Reputation Protocol for IoV Applications.</p>
Data from: Large-scale cooperation driven by reputation, not fear of divine punishment
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Data from: Heterozygosity-fitness correlations in zebra finches: microsatellite markers can be better than their reputation
Numerous studies have reported associations between heterozygosity in microsatellite markers and fitness-related traits (heterozygosity-fitness correlations, HFCs). However, it has often been questioned whether HFCs reflect general inbreeding depression, because a small panel of microsatellite markers does not reflect very well an individual's inbreeding coefficient (F) as calculated from a pedigree. Here we challenge this prevailing view. Due to chance events during Mendelian segregation, an individual's realized proportion of the genome that is identical by descent (IBD) may substantially deviate from the pedigree-based expectation (i.e. F). This Mendelian noise may result in a weak correlation between F and multi-locus heterozygosity, but this does not imply that multi-locus heterozygosity is a bad estimator of realized IBD. We examined correlations between 11 fitness-related traits measured in up to 1,192 captive zebra finches and three measures of inbreeding: (1) heterozygosity across 11 microsatellite markers, (2) heterozygosity across 1,359 SNP markers, and (3) F, based on a 5-generation pedigree. All 11 phenotypic traits showed positive relationships with measures of heterozygosity, especially traits that are most closely related to fitness. Remarkably, the small panel of microsatellite markers produced equally strong HFCs as the large panel of SNP markers, and both marker-based approaches produced stronger correlations with phenotypes than the pedigree-based F. We argue that a small panel of microsatellites with high allelic richness may better reflect an individual's realized IBD than previously appreciated, especially in species like the zebra finch, where much of the genome is inherited in large blocks that rarely experience cross-over during meiosis.
Data from: The influence of social preferences and reputational concerns on intergroup prosocial behavior in gains and losses contexts
To what extent do people help ingroup members based on a social preference to improve ingroup members' outcomes, versus strategic concerns about preserving their reputation within their group? And do these motives manifest differently when a prosocial behaviour occurs in the context of helping another gain a positive outcome (study 1), versus helping another to avoid losing a positive outcome (study 2)? In both contexts, we find that participants are more prosocial towards ingroup (versus outgroup members) and more prosocial when decisions are public (versus private) but find no interaction between group membership and either anonymity of the decision or expected economic value of helping. Therefore, consistent with a preference-based account of ingroup favouritism, people appear to prefer to help ingroup members more than outgroup members, regardless of whether helping can improve their reputation within their group. Moreover, this preference to help ingroup members appears to take the form of an intuitive social heuristic to help ingroup members, regardless of the economic incentives or possibility of reputation management. Theoretical and practical implications for the study of intergroup prosocial behaviour are discussed.
Data from: Heterozygosity-fitness correlations in zebra finches: microsatellite markers can be better than their reputation
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Data from: The influence of social preferences and reputational concerns on intergroup prosocial behavior in gains and losses contexts
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Data from: The emergence and selection of reputation systems that drive cooperative behaviour
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How to discipline financial markets: reputation is not enough
<p>This package contains the data, programs and instructions to replicate manuscript "How to discipline financial markets: reputation is not enough" by Maria Bigoni, Gabriele Camera and Marco Casari forthcoming at JEEA.</p>
Data from: Human cooperation based on punishment reputation
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