Experimental implementations of the Ultimatum Game are some of the most well studied economic experiments of the last twenty years. There are two popular explanations for why Proposers offer substantially more than the smallest positive amount of the pie. One is that the Proposers have other-regarding preferences and the other explanation is that Proposers are selfish, but fear rejection by Responders who will reject low offers. Most experiments that attempt to discriminate between these two explanations contrast behavior in the Ultimatum Game to behavior in the Dictator Game. The Dictator Game removes strategic concerns from the Ultimatum Game without substantially changing the predicted behavior of a selfish Proposer. Researchers thus believe that subtracting Dictator Game offers from Ultimatum Game offers isolates the fraction of average offers in the Ultimatum Game motivated by other-regarding preferences. In most Dictator Game experiments, Proposers offer less than they do in Ultimatum Games, but they still offer non-trivial positive amounts. This result has led analysts to posit that Proposer behavior in the Ultimatum Game is motivated in part by other-regarding preferences. There are, however, potential problems in drawing inferences about Proposer behavior in the Ultimatum Game from observations of Proposer behavior in the Dictator Game. First, it is well known that objectively irrelevant contextual details in experiments can affect subject behavior in systematic ways. Second, altruistic motivations are less costly to satisfy per monetary unit in the Ultimatum Game because each monetary unit offered to the Responder reduces the probability of rejection. Thus strategic motivations may be sufficient to explain behavior in the Ultimatum Game. In other words, a Proposer with altruistic preferences may offer the same amount of money as an identical Proposer who differs only in his lack of such preferences. In contrast to previous approaches that remove the strategic incentives in the Ultimatum Game, we remove the incentives for expressing other-regarding preferences. We do so through a treatment in which humans are paired with robots that, for each choice in the Proposer’s decision space, reject with the same frequency as humans in previous experiments. Proposers are aware they are playing with automata that are programmed to reject and accept as humans have done in previous implementations of the experiment. Under the mild assumption that humans do not express other-regarding preferences for fictional automata, this treatment presents an Ultimatum Game with only strategic motives operative. Note also that unlike previous attempts that use a different game to make inferences about behavior in the Ultimatum Game, we are able to measure the effects of strategic and other-regarding motives without changing the fundamental structure of the Ultimatum Game. Moreover, previous analyses do not formally include decision error as an important motivation for non-SPNE offers. To test for misunderstanding of the strategic environment, we develop a second treatment in which subjects play the Ultimatum Game with a robot Responder that rejects or accepts every offer with equal probability. If Proposers are truly thinking about Responder rejection rates in formulating their offers, they should offer $0 in this treatment.

Theories that formally describe decision-making have traditionally posited that decisions are made by rational actors. However, it is generally accepted that humans often make irrational decisions because of quick emotional judgements. In order to reconcile these two inconsistent ideas psychologists have developed two-system theories that hypothesize decisions are made by two opposing cognitive systems, representing the rational and emotional processing of decisions. Evidence for a two-system model of decision-making can be observed in ultimatum game responder decisions. It is thought that rational processing of these choices will produce acceptance of unfair offers and emotional processing will encourage rejection of unfair offers. Emotional priming has been shown to decrease ultimatum game acceptances and trans-cranial magnetic stimulation of rational brain areas, i.e. DLPFC, show increases in ultimatum game acceptances. This study investigated the possibility of using behavioral tasks that are known to activate rational brain areas to promote/disrupt ultimatum game acceptances. The possible relationship between ultimatum game acceptances and executive functions was also examined. Although there were promising indications that working memory loading may increase ultimatum game acceptances in between-subject experiments, a within-subject investigation found little support for this method of promoting/disrupting rational ultimatum game decisions. There were also no relationships found between switching or inhibition executive functions and ultimatum game responder decisions. A moderate positive relationship was found between updating executive function and ultimatum game acceptance rates but this relationship was dependent on working memory task feedback, a within-subject design and active loading of the working memory system. However, its possible that these findings only apply to within-subject paradigms and future between-subject studies are advised.

Punishment is the act of penalizing an individual as a response to a transgression. This thesis will deal with punishment in experimental game scenarios and in experimental criminal punishment scenarios, along with their different adaptations. The aim will be to provide an overview of both psychological and neurological underpinnings of punishment by reviewing existing literature. While punishment ought to deter transgressions and promote cooperative behavior, internal neural reward-related systems seem to be a driving factor of the desire to punish wrongdoings. Decisions on whether a transgressor is guilty and deserves punishment is mediated by the medial prefrontal cortex with an emphasis on the ventromedial parts. External influences affect the behavioral output and its underlying neural signatures of punishment. Social context such as peer pressure and in-group bias emphasize the importance of theory of mind related areas when conducting punishment.

Fairness is an important topic that has captured the interest of researchers in many fields. Looking at behavioural and experimental economics, various methods have been used to shed the light on fairness. One of the most recognizable ways is through the use of the ultimatum game. In this paper, we aimed to look at fairness considerations by utilizing this game, while also highlighting gender and culture as factors of importance. Two models, specifically Rabin’s reciprocity model and Fehr and Schmidt’s inequity aversion model, in addition to various research papers on the topics of gender and culture, were used as the cornerstones for this paper. The experimental design was a replication of the ultimatum game, which was carried out online. Results showed that there were noticeable effects from both gender and culture on the behaviour of the participants in the game. However, the results were not statistically significant enough to be considered as determining factors in regard to why individuals behave in a specific manner in the game. Since research from the field has reached mixed conclusions, we can say that making inferences about human behaviour is harder than one might think.

No description available.

Nesta dissertação é explorado o comportamento emergente de uma população heterogênea de jogadores negociando segundo o jogo do ultimato: dois jogadores recebem uma oferta; um deles (o proponente) propõe a sua divisão, enquanto o outro jogador (o aceitador) pode aceitar ou rejeitar a proposta. A rejeição é prejudicial a ambos jogadores, pois nenhum deles recebe sua parcela dos possíveis ganhos. Neste contexto, o ganho e seus momentos são calculados a partir de métodos analíticos simples e várias simulações computacionais corroboram os resultados obtidos. Também são analisadas as flutuações estatísticas da distribuição do ganho. Além disso, é apresentada uma abordagem simples evolucionária que considera mudanças em estratégias baseadas em ganhos anteriores. Para este caso, é demonstrado que o tempo médio de permanência (idade) de uma estratégia de uma população de "justos" convergepara um valor constante enquanto t se aproxima do ∞ e o cutoff médio decai segundouma lei de potência em tempos altos, após uma queda inicial. Também foram observadas transições entre comportamentos de alto e baixo ganho. Adicionalmente foi estudadauma versão espacial desse modelo. Para tanto são consideradosjogadores interagindo com seus primeiros vizinhos em reticulados 2D de acordo com duas dinâmicas estocáticas: (1) morte e nascimento com amostragem seletiva (MNAS), (2) Gibbs sampling sobre a vizinhança (GS). Estes resultados trazem importantes considerações sobre o projeto de simulaçõesno contexto da teoria dos jogos evolucionários, em particular na simulação dos aspectos relevantes quando modelando grandes populações. / Weexplore the emergent behavior of a heterogeneous population of players negotiating via an ultimatum game: two players are offered a gift; one of them (the proposer) suggestshow to divide the offer while the other player (the responder) can either agree or reject the deal. Rejection is detrimental to both players as it results in no eamings. In this context, the payoff and its moments are calculated from simple analytical methods and several computer simulations corroborate the obtained results. Wealso analyze statistical fluctuationson payoff distribution. In addition,we present a simple evolutionaryapproach that considers changes in strategies based on previous eamings. For this case, we show that average permanence time (age) in a strategy of a fair population converges to a constant value when t approaches ∞ and the cutoff average decays as a power law for large times after a initial deterministic slip. We have also observed transitions between highlow payoffbehaviors. Additionallywe studied a spatial version ofthis model. For this we consider players interacting with their nearest neighbors in 2D lattices according to two different stochastic dynamics: (1) Death and birth with selective sampling (MNAS), (2) Gibbs sampling on neighborhood (GS) Webelieve that these results can bring important considerationsto the design of simulations in the context ofthe evolutionary game theory, in particular in the simulation of relevant features when modeling large populations.

No description available.

Nesta dissertação é explorado o comportamento emergente de uma população heterogênea de jogadores negociando segundo o jogo do ultimato: dois jogadores recebem uma oferta; um deles (o proponente) propõe a sua divisão, enquanto o outro jogador (o aceitador) pode aceitar ou rejeitar a proposta. A rejeição é prejudicial a ambos jogadores, pois nenhum deles recebe sua parcela dos possíveis ganhos. Neste contexto, o ganho e seus momentos são calculados a partir de métodos analíticos simples e várias simulações computacionais corroboram os resultados obtidos. Também são analisadas as flutuações estatísticas da distribuição do ganho. Além disso, é apresentada uma abordagem simples evolucionária que considera mudanças em estratégias baseadas em ganhos anteriores. Para este caso, é demonstrado que o tempo médio de permanência (idade) de uma estratégia de uma população de "justos" convergepara um valor constante enquanto t se aproxima do ∞ e o cutoff médio decai segundouma lei de potência em tempos altos, após uma queda inicial. Também foram observadas transições entre comportamentos de alto e baixo ganho. Adicionalmente foi estudadauma versão espacial desse modelo. Para tanto são consideradosjogadores interagindo com seus primeiros vizinhos em reticulados 2D de acordo com duas dinâmicas estocáticas: (1) morte e nascimento com amostragem seletiva (MNAS), (2) Gibbs sampling sobre a vizinhança (GS). Estes resultados trazem importantes considerações sobre o projeto de simulaçõesno contexto da teoria dos jogos evolucionários, em particular na simulação dos aspectos relevantes quando modelando grandes populações. / Weexplore the emergent behavior of a heterogeneous population of players negotiating via an ultimatum game: two players are offered a gift; one of them (the proposer) suggestshow to divide the offer while the other player (the responder) can either agree or reject the deal. Rejection is detrimental to both players as it results in no eamings. In this context, the payoff and its moments are calculated from simple analytical methods and several computer simulations corroborate the obtained results. Wealso analyze statistical fluctuationson payoff distribution. In addition,we present a simple evolutionaryapproach that considers changes in strategies based on previous eamings. For this case, we show that average permanence time (age) in a strategy of a fair population converges to a constant value when t approaches ∞ and the cutoff average decays as a power law for large times after a initial deterministic slip. We have also observed transitions between highlow payoffbehaviors. Additionallywe studied a spatial version ofthis model. For this we consider players interacting with their nearest neighbors in 2D lattices according to two different stochastic dynamics: (1) Death and birth with selective sampling (MNAS), (2) Gibbs sampling on neighborhood (GS) Webelieve that these results can bring important considerationsto the design of simulations in the context ofthe evolutionary game theory, in particular in the simulation of relevant features when modeling large populations.

Nesta dissertação é explorado o comportamento emergente de uma população heterogênea de jogadores negociando segundo o jogo do ultimato: dois jogadores recebem uma oferta; um deles (o proponente) propõe a sua divisão, enquanto o outro jogador (o aceitador) pode aceitar ou rejeitar a proposta. A rejeição é prejudicial a ambos jogadores, pois nenhum deles recebe sua parcela dos possíveis ganhos. Neste contexto, o ganho e seus momentos são calculados a partir de métodos analíticos simples e várias simulações computacionais corroboram os resultados obtidos. Também são analisadas as flutuações estatísticas da distribuição do ganho. Além disso, é apresentada uma abordagem simples evolucionária que considera mudanças em estratégias baseadas em ganhos anteriores. Para este caso, é demonstrado que o tempo médio de permanência (idade) de uma estratégia de uma população de "justos" convergepara um valor constante enquanto t se aproxima do ∞ e o cutoff médio decai segundouma lei de potência em tempos altos, após uma queda inicial. Também foram observadas transições entre comportamentos de alto e baixo ganho. Adicionalmente foi estudadauma versão espacial desse modelo. Para tanto são consideradosjogadores interagindo com seus primeiros vizinhos em reticulados 2D de acordo com duas dinâmicas estocáticas: (1) morte e nascimento com amostragem seletiva (MNAS), (2) Gibbs sampling sobre a vizinhança (GS). Estes resultados trazem importantes considerações sobre o projeto de simulaçõesno contexto da teoria dos jogos evolucionários, em particular na simulação dos aspectos relevantes quando modelando grandes populações. / Weexplore the emergent behavior of a heterogeneous population of players negotiating via an ultimatum game: two players are offered a gift; one of them (the proposer) suggestshow to divide the offer while the other player (the responder) can either agree or reject the deal. Rejection is detrimental to both players as it results in no eamings. In this context, the payoff and its moments are calculated from simple analytical methods and several computer simulations corroborate the obtained results. Wealso analyze statistical fluctuationson payoff distribution. In addition,we present a simple evolutionaryapproach that considers changes in strategies based on previous eamings. For this case, we show that average permanence time (age) in a strategy of a fair population converges to a constant value when t approaches ∞ and the cutoff average decays as a power law for large times after a initial deterministic slip. We have also observed transitions between highlow payoffbehaviors. Additionallywe studied a spatial version ofthis model. For this we consider players interacting with their nearest neighbors in 2D lattices according to two different stochastic dynamics: (1) Death and birth with selective sampling (MNAS), (2) Gibbs sampling on neighborhood (GS) Webelieve that these results can bring important considerationsto the design of simulations in the context ofthe evolutionary game theory, in particular in the simulation of relevant features when modeling large populations.

View of rational choice in coherence with ultimatum game. Rational choice theory adjusted by joining behavioral economics to better comprehend decision-making processes. In this thesis I focus on researching strong influences using multiple simple games. Namely: ultimatum game, dictator game and modifications of previous. They will be carried out by online questionnaire. Next step will be analysis of components to determine significant ones and impact of game's modifications. I assume that decision-maker's rationality will be disproven. This thesis will continue with assessing influence of risk and fear of loss. Goal is to gather enough of detailed data about motivations and participants in order to perform analysis and establish statistically significant influences.