A comment on "Rational learning lead to nash equilibrium" by professors Ehud Kalai and Ehud Lehrer

Kalai and Lebrer (93a, b) have recently show that for the case of infinitely repeated games, a coordination assumption on beliefs and optimal strategies ensures convergence to Nash equilibrium. In this paper, we show that for the case of repeated games with long (but finite) horizon, their condition does not imply approximate Nash equilibrium play. Recently Kalai and Lehrer (93a, b) proved that a coordination assumption on beliefs and optimal strategies, ensures that pIayers of an infinitely repeated game eventually pIay 'E-close" to an E-Nash equilibrium. Their coordination assumption requires that if players believes that certain set of outcomes have positive probability then it must be the case that this set of outcomes have, in fact, positive probability. This coordination assumption is called absolute continuity. For the case of finitely repeated games, the absolute continuity assumption is a quite innocuous assumption that just ensures that pIayers' can revise their priors by Bayes' Law. However, for the case of infinitely repeated games, the absolute continuity assumption is a stronger requirement because it also refers to events that can never be observed in finite time.

Elaboração de um curso interativo voltado ao aprendizado de um sistema aplicativo em Odontologia, utilizando a plataforma Moodle

In universities, in any type of faculty or research institute, it is possible to observe innumerable informatics applications: database management, organization of catalogues, expediting diagnoses and many other applications. Within this context, distance learning could facilitate the learning of various professions. Distance education is the process of teaching-learning and the professional has to be prepared to deal with the new tools that are made available, using them to his/her benefit, and consequently in his/her professional capacity, to guarantee greater productivity and interaction with the various spheres in the work field. Methods: The aim of this research is to develop a course on the use of the Dentistry software application EasyDental, using the teaching at a distance methodology by means of the Moodle platform. The proposed course was composed of 14 modules, in order to enable a progressive and a rational learning. Conclusion: It could be concluded that this platform allowed the preparation of the proposed distance course, in a practical and versatile manner, considering the resources and activities it has. However, to use this platform, one must have dominion of the basic concepts of informatics, and the tools of the Moodle platform.

Um estudo dos avanços conceituais dos estudantes sobre calor e temperatura decorrentes da aplicação de uma estratégia de ensino inspirada na teoria de Lakatos

We report here a study about the application of a formal teaching strategy about heat and temperature concepts applied among high school students. The strategy belongs to a research trend which deals with epistemological analogies and has an innovative character related to the students’ preparation for rational debates between conceptions and/or rival theories, using didactical rational reconstruction (DRR) which aims to help scientific concepts rational learning. We investigate the outcomes of this preparation and students’ conceptual development, analyzed through a qualitative-interpretative methodological approach.

Tales of science and defiance : the case for co-learning and collaboration in bridging the science/emotion divide in water recycling debates

Although science is generally assumed to be well integrated into rational decision-making models, it can be used to destabilise consultative processes, particularly when emotions are involved. Water policies are often seen as debates over technical and engineering issues, but can be highly controversial. Recycled water proposals, in particular, can create highly emotive conflicts. Through a case study regarding the rejection of recycled water proposals in the south-east Queensland, Australia, we explore the influence of science and emotions in contemporary water planning. We highlight the dangers inherent in promoting technical water planning issues at the expense of appropriate consideration of citizen concerns. Combining the science–policy interface and stakeholder engagement literatures, we advocate for collaborative decision-making processes that accommodate emotions and value judgements. A more collaborative stakeholder engagement model, founded on the principles of co-learning, has the potential to broaden the decision-making base and to promote better and more inclusive decision-making.

Absolutely expedient algorithms for learning Nash equilibria

This paper considers a multi-person discrete game with random payoffs. The distribution of the random payoff is unknown to the players and further none of the players know the strategies or the actual moves of other players. A class of absolutely expedient learning algorithms for the game based on a decentralised team of Learning Automata is presented. These algorithms correspond, in some sense, to rational behaviour on the part of the players. All stable stationary points of the algorithm are shown to be Nash equilibria for the game. It is also shown that under some additional constraints on the game, the team will always converge to a Nash equilibrium.

Bidding Dynamics of Rational Advertisers in Sponsored Search Auctions on the Web

In this paper, we address a key problem faced by advertisers in sponsored search auctions on the web: how much to bid, given the bids of the other advertisers, so as to maximize individual payoffs? Assuming the generalized second price auction as the auction mechanism, we formulate this problem in the framework of an infinite horizon alternative-move game of advertiser bidding behavior. For a sponsored search auction involving two advertisers, we characterize all the pure strategy and mixed strategy Nash equilibria. We also prove that the bid prices will lead to a Nash equilibrium, if the advertisers follow a myopic best response bidding strategy. Following this, we investigate the bidding behavior of the advertisers if they use Q-learning. We discover empirically an interesting trend that the Q-values converge even if both the advertisers learn simultaneously.

Performance Modeling based on Multidimensional Surface Learning for Performance Predictions of Parallel Applications in Non-Dedicated Environments

Modeling the performance behavior of parallel applications to predict the execution times of the applications for larger problem sizes and number of processors has been an active area of research for several years. The existing curve fitting strategies for performance modeling utilize data from experiments that are conducted under uniform loading conditions. Hence the accuracy of these models degrade when the load conditions on the machines and network change. In this paper, we analyze a curve fitting model that attempts to predict execution times for any load conditions that may exist on the systems during application execution. Based on the experiments conducted with the model for a parallel eigenvalue problem, we propose a multi-dimensional curve-fitting model based on rational polynomials for performance predictions of parallel applications in non-dedicated environments. We used the rational polynomial based model to predict execution times for 2 other parallel applications on systems with large load dynamics. In all the cases, the model gave good predictions of execution times with average percentage prediction errors of less than 20%

Learning design contingent valuation (LDCV): NOAA guidelines, preference learning and coherent arbitrariness

We extend the contingent valuation (CV) method to test three differing conceptions of individuals' preferences as either (i) a-priori well-formed or readily divined and revealed through a single dichotomous choice question (as per the NOAA CV guidelines [K. Arrow, R. Solow, P.R. Portney, E.E. Learner, R. Radner, H. Schuman, Report of the NOAA panel on contingent valuation, Fed. Reg. 58 (1993) 4601-4614]); (ii) learned or 'discovered' through a process of repetition and experience [J.A. List, Does market experience eliminate market anomalies? Q. J. Econ. (2003) 41-72; C.R. Plott, Rational individual behaviour in markets and social choice processes: the discovered preference hypothesis, in: K. Arrow, E. Colombatto, M. Perleman, C. Schmidt (Eds.), Rational Foundations of Economic Behaviour, Macmillan, London, St. Martin's, New York, 1996, pp. 225-250]; (iii) internally coherent but strongly influenced by some initial arbitrary anchor [D. Ariely, G. Loewenstein, D. Prelec, 'Coherent arbitrariness': stable demand curves without stable preferences, Q. J. Econ. 118(l) (2003) 73-105]. Findings reject both the first and last of these conceptions in favour of a model in which preferences converge towards standard expectations through a process of repetition and learning. In doing so, we show that such a 'learning design CV method overturns the 'stylised facts' of bias and anchoring within the double bound dichotomous choice elicitation format. (C) 2007 Elsevier Inc. All rights reserved.

Testing preference formation in learning design contingent valuation (LDCV) using advanced information and repetitive treatments

Policymakers have largely replaced Single Bounded Discrete Choice (SBDC) valuation by the more statistically efficient repetitive methods; Double Bounded Discrete Choice (DBDC) and Discrete Choice Experiments (DCE) . Repetitive valuation permits classification into rational preferences: (i) a priori well-formed; (ii) consistent non-arbitrary values “discovered” through repetition and experience; (Plott, 1996; List 2003) and irrational preferences; (iii) consistent but arbitrary values as “shaped” by preceding bid level (Tufano, 2010; Ariely et al., 2003) and (iv) inconsistent and arbitrary values. Policy valuations should demonstrate behaviorally rational preferences. We outline novel methods for testing this in DBDC applied to renewable energy premiums in Chile.

Learning, fatigue and preference formation in discrete choice experiments

While the repeated nature of Discrete Choice Experiments is advantageous from a sampling efficiency perspective, patterns of choice may differ across the tasks, due, in part, to learning and fatigue. Using probabilistic decision process models, we find in a field study that learning and fatigue behavior may only be exhibited by a small subset of respondents. Most respondents in our sample show preference and variance stability consistent with rational pre-existent and
well formed preferences. Nearly all of the remainder exhibit both learning and fatigue effects. An important aspect of our approach is that it enables learning and fatigue effects to be explored, even though they were not envisaged during survey design or data collection.

Vignette 6 Pluridisciplinary learning and assessment : reflections on practice

While designing group assessment for student learning outcomes is always difficult, the task is made more challenging in an interdisciplinary context. How much focus should be placed on assessment of discipline-specific knowledge, how much on the interdisciplinary knowledge that emerges as students work together in a non-linear, co-rational design and how much on the group dynamic (generic capabilities) being developed? While additional learning outcomes can be expected from the activities in which students engage in an interdisciplinary context, there is also an expectation, particularly for disciplines such as accounting, engineering and architecture where courses are professionally accredited, that discipline-specific learning outcomes are not compromised. This vignette presents some of the complexities that surfaced during the implementation of a pilot course designed as an experiential real world of work challenge for student.

Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.

Re-making jobs : enacting and learning work practices

This paper takes up understandings of organisations where practices constitute and frame past and present work, as well as future work practice possibilities. Within this view, work practices, and thus organisations, are both perpetuated and varied through employees’ enactments of work. Using a practice lens, we are particularly interested in the ways workers simultaneously maintain and alter practices in their workplace—we characterise this as re-making one’s job. This perspective challenges ways in which managers often depict jobs and everyday work—as rational, linear and easily describable. We suggest that workers at various levels of responsibility contribute more to the formation of organisational practices than is often assumed. The processes of re-making jobs and remaking organisational practices create tensions that we posit as sites for learning. This paper addresses these issues through a focus on work practices in two Australian organisations that have been undergoing significant cultural change.