Groupwise information sharing promotes ingroup favoritism in indirect reciprocity

Indirect reciprocity is a mechanism for cooperation in social dilemma situations, in which an individual is motivated to help another to acquire a good reputation and receive help from others afterwards. Ingroup favoritism is another aspect of human cooperation, whereby individuals help members in their own group more often than those in other groups. Ingroup favoritism is a puzzle for the theory of cooperation because it is not easily evolutionarily stable. In the context of indirect reciprocity, ingroup favoritism has been shown to be a consequence of employing a double standard when assigning reputations to ingroup and outgroup members; e.g., helping an ingroup member is regarded as good, whereas the same action toward an outgroup member is regarded as bad. We analyze a model of indirect reciprocity in which information sharing is conducted groupwise. In our model, individuals play social dilemma games within and across groups, and the information about their reputations is shared within each group. We show that evolutionarily stable ingroup favoritism emerges even if all the players use the same reputation assignment rule regardless of group (i.e., a single standard). Two reputation assignment rules called simple standing and stern judging yield ingroup favoritism. Stern judging induces much stronger ingroup favoritism than does simple standing. Simple standing and stern judging are evolutionarily stable against each other when groups employing different assignment rules compete and the number of groups is sufficiently large. In addition, we analytically show as a limiting case that homogeneous populations of reciprocators that use reputations are unstable when individuals independently infer reputations of individuals, which is consistent with previously reported numerical results.Comment: 25 pages, 7 figures. The Abstract is shortened to fill in arXiv's abstract for

Coevolution of trustful buyers and cooperative sellers in the trust game

Many online marketplaces enjoy great success. Buyers and sellers in successful markets carry out cooperative transactions even if they do not know each other in advance and a moral hazard exists. An indispensable component that enables cooperation in such social dilemma situations is the reputation system. Under the reputation system, a buyer can avoid transacting with a seller with a bad reputation. A transaction in online marketplaces is better modeled by the trust game than other social dilemma games, including the donation game and the prisoner's dilemma. In addition, most individuals participate mostly as buyers or sellers; each individual does not play the two roles with equal probability. Although the reputation mechanism is known to be able to remove the moral hazard in games with asymmetric roles, competition between different strategies and population dynamics of such a game are not sufficiently understood. On the other hand, existing models of reputation-based cooperation, also known as indirect reciprocity, are based on the symmetric donation game. We analyze the trust game with two fixed roles, where trustees (i.e., sellers) but not investors (i.e., buyers) possess reputation scores. We study the equilibria and the replicator dynamics of the game. We show that the reputation mechanism enables cooperation between unacquainted buyers and sellers under fairly generous conditions, even when such a cooperative equilibrium coexists with an asocial equilibrium in which buyers do not buy and sellers cheat. In addition, we show that not many buyers may care about the seller's reputation under cooperative equilibrium. Buyers' trusting behavior and sellers' reputation-driven cooperative behavior coevolve to alleviate the social dilemma.Comment: 5 figure

Indirect reciprocity in three types of social dilemmas.

Indirect reciprocity is a key mechanism for the evolution of human cooperation. Previous studies explored indirect reciprocity in the so-called donation game, a special class of Prisoner\u27s Dilemma (PD) with unilateral decision making. A more general class of social dilemmas includes Snowdrift (SG), Stag Hunt (SH), and PD games, where two players perform actions simultaneously. In these simultaneous-move games, moral assessments need to be more complex; for example, how should we evaluate defection against an ill-reputed, but now cooperative, player? We examined indirect reciprocity in the three social dilemmas and identified twelve successful social norms for moral assessments. These successful norms have different principles in different dilemmas for suppressing cheaters. To suppress defectors, any defection against good players is prohibited in SG and PD, whereas defection against good players may be allowed in SH. To suppress unconditional cooperators, who help anyone and thereby indirectly contribute to jeopardizing indirect reciprocity, we found two mechanisms: indiscrimination between actions toward bad players (feasible in SG and PD) or punishment for cooperation with bad players (effective in any social dilemma). Moreover, we discovered that social norms that unfairly favor reciprocators enhance robustness of cooperation in SH, whereby reciprocators never lose their good reputation

Predictability of conversation partners

Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information theoretic method to the spatiotemporal data of cell-phone locations, [C. Song et al. Science 327, 1018 (2010)] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one's conversation partners is defined as the degree to which one's next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to some extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by long-tailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual's predictability is correlated with the position in the static social network derived from the data. Individuals confined in a community - in the sense of an abundance of surrounding triangles - tend to have low predictability, and those bridging different communities tend to have high predictability.Comment: 38 pages, 19 figure

Construction of Shape Atlas for Abdominal Organs using Three-Dimensional Mesh Variational Autoencoder

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 24-27 July 2023, Sydney, AustraliaA model that represents the shapes and positions of organs or skeletal structures with a small number of parameters may be expected to have a wide range of clinical applications, such as radiotherapy and surgical guidance. However, because soft organs vary in shape and position between patients, it is difficult for linear models to reconstruct locally variable shapes, and nonlinear models are prone to overfitting, particularly when the quantity of data is small. The aim of this study was to construct a shape atlas with high accuracy and good generalization performance. We designed a mesh variational autoencoder that can reconstruct both nonlinear shape and position with high accuracy. We validated the trained model for liver meshes of 125 cases, and found that it was possible to reconstruct the positions and shapes with an average accuracy of 4.3 mm for the test data of 19 cases

HPLC Analysis of Homocysteine and Related Compounds

Homocysteine (Hcy), a sulfur-containing amino acid, is a representative intermediate metabolite of methionine (Met) to cysteine (Cys) via several intermediates. An elevated level of Hcy in plasma plays an important role in diseases such as neural tube defects and Down syndrome. Homocystinuria is the most common inborn error of sulfur metabolism and is caused by mutations in the metabolic enzymes of Hcy. These errors can be caused by abnormal levels of Met metabolites and classified on the basis of plasma Met levels. Additionally, Hcy and related compounds such as glutathione play an important role in maintaining homeostasis. Therefore, the simultaneous determination of Hcy and/or related compounds is required for appropriate clinical management of several diseases. The sulfur-containing amino acids and their derivatives in biological samples are quantified sensitively using high-performance liquid chromatography methods coupled with various detection methods such as UV/Vis, fluorescence, chemiluminescence, electrochemical, mass spectrometry, and tandem mass spectrometry. In this chapter, we review recent advances in these analytical methods and their applications

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy

Rare Third-Party Punishment Promotes Cooperation in Risk-Averse Social Learning Dynamics

Third-party punishment is a common mechanism to promote cooperation in humans. Theoretical models of evolution of cooperation predict that punishment maintains cooperation if it is sufficiently frequent. On the other hand, empirical studies have found that participants frequently punishing others do not success in comparison with those not eager to punish others, suggesting that punishment is suboptimal and thus should not be frequent. That being the case, our question is what mechanism, if any, can sustain cooperation even if punishment is rare. The present study proposes that one possible mechanism is risk-averse social learning. Using the method of evolutionary game dynamics, we investigate the effect of risk attitude of individuals on the question. In our framework, individuals select a strategy based on its risk, i.e., the variance of the payoff, as well as its expected payoff; risk-averse individuals prefer to select a strategy with low variable payoff. Using the framework, we examine the evolution of cooperation in two-player social dilemma games with punishment. We study two models: cooperators and defectors compete, while defectors may be punished by an exogenous authority; and cooperators, defectors, and cooperative punishers compete, while defectors may be punished by the cooperative punishers. We find that in both models, risk-averse individuals achieve stable cooperation with significantly low frequency of punishment. We also examine three punishment variants: in each game, all defectors are punished; only one of defectors is punished; and only a defector who exploits a cooperator or a cooperative punisher is punished. We find that the first and second variants effectively promote cooperation. Comparing the first and second variants, each can be more effective than the other depending on punishment frequency

2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs

Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in two-dimensional X-ray images. We propose a supervised deep learning framework that achieves 2D/3D deformable image registration between 3D volumes and single-viewpoint 2D projected images. The proposed method learns the translation from the target 2D projection images and the initial 3D volume to 3D displacement fields. In experiments, we registered 3D-computed tomography (CT) volumes to digitally reconstructed radiographs generated from abdominal 4D-CT volumes. For validation, we used 4D-CT volumes of 35 cases and confirmed that the 3D-CT volumes reflecting the nonlinear and local respiratory organ displacement were reconstructed. The proposed method demonstrate the compatible performance to the conventional methods with a dice similarity coefficient of 91.6 \% for the liver region and 85.9 \% for the stomach region, while estimating a significantly more accurate CT values

Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization

Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion