Reputation and honesty in a market for information

Previous works on asymmetric information in asset markets tendto focus on the potential gains in the asset market itself. We focus on the market for information and conduct an experimental study to explore, in a game of finite but uncertain duration, whether reputation can be an effective constraint on deliberate misinformation. At the beginning of each period, an uninformed potential asset buyer can purchase information, at a fixed price and from a fully-informed source, about the value of the asset in that period. The informational insiders cannot purchase the asset and are given short-term incentives to provide false information when the asset value is low. Our model predicts that, in accordance with the Folk Theorem, Pareto-superior outcomes featuring truthful revelation should be sustainable. However, this depends critically on beliefs about rationality and behavior. We find that, overall, sellers are truthful 89% of the time. More significantly, the observed frequency of truthfulness is 81% when the asset value is low. Our result is consistent with both mixed-strategy and trigger strategy interpretations and provides evidence that most subjects correctly anticipate rational behavior. We discuss applications to financial markets, media regulation, and the stability of cartels.

MGMT promoter methylation in malignant gliomas: ready for personalized medicine?

The DNA repair enzyme O(6)-methylguanine-DNA methyltransferase (MGMT) antagonizes the genotoxic effects of alkylating agents. MGMT promoter methylation is the key mechanism of MGMT gene silencing and predicts a favorable outcome in patients with glioblastoma who are exposed to alkylating agent chemotherapy. This biomarker is on the verge of entering clinical decision-making and is currently used to stratify or even select glioblastoma patients for clinical trials. In other subtypes of glioma, such as anaplastic gliomas, the relevance of MGMT promoter methylation might extend beyond the prediction of chemosensitivity, and could reflect a distinct molecular profile. Here, we review the most commonly used assays for evaluation of MGMT status, outline the prerequisites for standardized tests, and evaluate reasons for difficulties in reproducibility. We critically discuss the prognostic and predictive value of MGMT silencing, reviewing trials in which patients with different types of glioma were treated with various chemotherapy schedules, either up-front or at recurrence. Standardization of MGMT testing requires comparison of different technologies across laboratories and prospectively validated cut-off values for prognostic or predictive effects. Moreover, future clinical trials will need to determine, for each subtype of glioma, the degree to which MGMT promoter methylation is predictive or prognostic, and whether testing should become routine clinical practice.

The evolution of punishment through reputation.

Punishment of non-cooperators has been observed to promote cooperation. Such punishment is an evolutionary puzzle because it is costly to the punisher while beneficial to others, for example, through increased social cohesion. Recent studies have concluded that punishing strategies usually pay less than some non-punishing strategies. These findings suggest that punishment could not have directly evolved to promote cooperation. However, while it is well established that reputation plays a key role in human cooperation, the simple threat from a reputation of being a punisher may not have been sufficiently explored yet in order to explain the evolution of costly punishment. Here, we first show analytically that punishment can lead to long-term benefits if it influences one's reputation and thereby makes the punisher more likely to receive help in future interactions. Then, in computer simulations, we incorporate up to 40 more complex strategies that use different kinds of reputations (e.g. from generous actions), or strategies that not only include punitive behaviours directed towards defectors but also towards cooperators for example. Our findings demonstrate that punishment can directly evolve through a simple reputation system. We conclude that reputation is crucial for the evolution of punishment by making a punisher more likely to receive help in future interactions, and that experiments investigating the beneficial effects of punishment in humans should include reputation as an explicit feature.

Identity [R]Evolution: Multi-Disciplinary Perpectives

The identity [r]evolution is happening. Who are you, who am I in the information society? In recent years, the convergence of several factors - technological, political, economic - has accelerated a fundamental change in our networked world. On a technological level, information becomes easier to gather, to store, to exchange and to process. The belief that more information brings more security has been a strong political driver to promote information gathering since September 11. Profiling intends to transform information into knowledge in order to anticipate one's behaviour, or needs, or preferences. It can lead to categorizations according to some specific risk criteria, for example, or to direct and personalized marketing. As a consequence, new forms of identities appear. They are not necessarily related to our names anymore. They are based on information, on traces that we leave when we act or interact, when we go somewhere or just stay in one place, or even sometimes when we make a choice. They are related to the SIM cards of our mobile phones, to our credit card numbers, to the pseudonyms that we use on the Internet, to our email addresses, to the IP addresses of our computers, to our profiles... Like traditional identities, these new forms of identities can allow us to distinguish an individual within a group of people, or describe this person as belonging to a community or a category. How far have we moved through this process? The identity [r]evolution is already becoming part of our daily lives. People are eager to share information with their "friends" in social networks like Facebook, in chat rooms, or in Second Life. Customers take advantage of the numerous bonus cards that are made available. Video surveillance is becoming the rule. In several countries, traditional ID documents are being replaced by biometric passports with RFID technologies. This raises several privacy issues and might actually even result in changing the perception of the concept of privacy itself, in particular by the younger generation. In the information society, our (partial) identities become the illusory masks that we choose -or that we are assigned- to interplay and communicate with each other. Rights, obligations, responsibilities, even reputation are increasingly associated with these masks. On the one hand, these masks become the key to access restricted information and to use services. On the other hand, in case of a fraud or negative reputation, the owner of such a mask can be penalized: doors remain closed, access to services is denied. Hence the current preoccupying growth of impersonation, identity-theft and other identity-related crimes. Where is the path of the identity [r]evolution leading us? The booklet is giving a glance on possible scenarios in the field of identity.

Exposure to superfluous information reduces cooperation and increases antisocial punishment in reputation-based interactions

Human cooperation is often based on reputation gained from previous interactions with third parties. Such reputation can be built on generous or punitive actions, and both, one's own reputation and the reputation of others have been shown to influence decision making in experimental games that control for confounding variables. Here we test how reputation-based cooperation and punishment react to disruption of the cognitive processing in different kinds of helping games with observers. Saying a few superfluous words before each interaction was used to possibly interfere with working memory. In a first set of experiments, where reputation could only be based on generosity, the disruption reduced the frequency of cooperation and lowered mean final payoffs. In a second set of experiments where reputation could only be based on punishment, the disruption increased the frequency of antisocial punishment (i.e. of punishing those who helped) and reduced the frequency of punishing defectors. Our findings suggest that working memory can easily be constraining in reputation-based interactions within experimental games, even if these games are based on a few simple rules with a visual display that provides all the information the subjects need to play the strategies predicted from current theory. Our findings also highlight a weakness of experimental games, namely that they can be very sensitive to environmental variation and that quantitative conclusions about antisocial punishment or other behavioral strategies can easily be misleading.

Personalized care in neuro-oncology coming of age: why we need MGMT and 1p/19q testing for malignant glioma patients in clinical practice.

Histological subtyping and grading by malignancy are the cornerstones of the World Health Organization (WHO) classification of tumors of the central nervous system. They shall provide clinicians with guidance as to the course of disease to be expected and the choices of treatment to be made. Nonetheless, patients with histologically identical tumors may have very different outcomes, notably in patients with astrocytic and oligodendroglial gliomas of WHO grades II and III. In gliomas of adulthood, 3 molecular markers have undergone extensive studies in recent years: 1p/19q chromosomal codeletion, O(6)-methylguanine methyltransferase (MGMT) promoter methylation, and mutations of isocitrate dehydrogenase (IDH) 1 and 2. However, the assessment of these molecular markers has so far not been implemented in clinical routine because of the lack of therapeutic implications. In fact, these markers were considered to be prognostic irrespective of whether patients were receiving radiotherapy (RT), chemotherapy, or both (1p/19q, IDH1/2), or of limited value because testing is too complex and no chemotherapy alternative to temozolomide was available (MGMT). In 2012, this situation has changed: long-term follow-up of the Radiation Therapy Oncology Group 9402 and European Organisation for Research and Treatment of Cancer 26951 trials demonstrated an overall survival benefit from the addition to RT of chemotherapy with procarbazine/CCNU/vincristine confined to patients with anaplastic oligodendroglial tumors with (vs without) 1p/19q codeletion. Furthermore, in elderly glioblastoma patients, the NOA-08 and the Nordic trial of RT alone versus temozolomide alone demonstrated a profound impact of MGMT promoter methylation on outcome by therapy and thus established MGMT as a predictive biomarker in this patient population. These recent results call for the routine implementation of 1p/19q and MGMT testing at least in subpopulations of malignant glioma patients and represent an encouraging step toward the development of personalized therapeutic approaches in neuro-oncology.

Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).

The role of reputation, punishment and their interplay in human cooperation

The problem of how cooperation can evolve between individuals or entities with conflicting interests is central to biology as many of the major evolutionary transitions, from the first replicating molecules to human societies, have required solving this problem. There are many routes to cooperation but humans seem to be distinct from other species as they have more complex and diverse mechanisms, often due to their higher cognitive skills, allowing them to reap the benefits from living in groups. Among those mechanisms, the use of reputation or past experience with others as well as sanctioning mechanisms both seem to be of major importance. They have often been considered separately but the interaction between the two might provide new insights as to how punishment could have appeared as a means to enforce cooperation in early humans. In this thesis, I firstly use theoretical approaches from evolutionary game theory to investigate the evolution of punishment and cooperation through a reputation system based on punitive actions, and compare the efficacy of this system, in terms of cooperation achieved, with one based on cooperative actions. On the other hand, I use empirical approaches from economics to test, in real life, predictions from theoretical models but also to explore further conditions such as environmental variation, constrained memory, or even the scale of competition between individuals. Both approaches have allowed contributing to the understanding of how these factors affect reputation and punishment use, and ultimately how cooperation is achieved.

Late side-effects after curative intent radiotherapy: Identification of hypersensitive patients for personalized strategy.

Radiation therapy undeniably enhances local control and thus improves overall survival in cancer patients. However, some long-term cancer survivors (less than 10%) develop severe late radio-induced toxicities altering their quality of life. Therefore, there is a need to identify patients who are sensitive to those toxicities and who could benefit from adapted care. In this review, we address all available techniques aiming to detect patients' hyper-radiosensitivity and present the scientific rationales these techniques are based on.

Personalized touristic routes implementing semantic web on GIS

Nowadays, when a user is planning a touristic route is very difficult to find out which are the best places to visit. The user has to choose considering his/her preferences due to the great quantity of information it is possible to find in the web and taking into account it is necessary to do a selection, within small time because there is a limited time to do a trip. In Itiner@ project, we aim to implement Semantic Web technology combined with Geographic Information Systems in order to offer personalized touristic routes around a region based on user preferences and time situation. Using ontologies it is possible to link, structure, share data and obtain the result more suitable for user's preferences and actual situation with less time and more precisely than without ontologies. To achieve these objectives we propose a web page combining a GIS server and a touristic ontology. As a step further, we also study how to extend this technology on mobile devices due to the raising interest and technological progress of these devices and location-based services, which allows the user to have all the route information on the hand when he/she does a touristic trip. We design a little application in order to apply the combination of GIS and Semantic Web in a mobile device.

Improving Searching and Browsing Capabilities of Learning Object Repositories

Learning object repositories are a basic piece of virtual learning environments used for content management. Nevertheless, learning objects have special characteristics that make traditional solutions for content management ine ective. In particular, browsing and searching for learning objects cannot be based on the typical authoritative meta-data used for describing content, such as author, title or publicationdate, among others. We propose to build a social layer on top of a learning object repository, providing nal users with additional services fordescribing, rating and curating learning objects from a teaching perspective. All these interactions among users, services and resources can be captured and further analyzed, so both browsing and searching can be personalized according to user pro le and the educational context, helping users to nd the most valuable resources for their learning process. In this paper we propose to use reputation schemes and collaborative filtering techniques for improving the user interface of a DSpace based learning object repository.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).