Cooperative strategies for imperfect CSI scenarios based on distributed Alamouti

Les comunicacions cooperatives estan guanyant un gran interès en les comunicacions modernes degut a que permeten millorar la transmissió dʼinformació entre un emissor i un receptor utilitzant una sèrie de terminals situats entre ells. Aquest projecte és un estudi complet del sistemes cooperatius, analitzant el seu rendiment i comparant lʼús dʼun sol dʼaquests terminals amb lʼús del codi Alamouti, que utilitza dos terminals. Primer hi ha una introducció als sistemes cooperatius i a la teoria de la informació. Després hem estudiat un sistema cooperatiu amb la teoria de la informació com a base, en termes de probabilitat de fallada del sistema, i posteriorment lʼhem adaptat a un sistema cooperatiu real utilitzant una modulació QPSK, estudiant la seva probabilitat dʼerror de paquet. Finalment es proposen diversos protocols que permeten millorar el rendiment del sistema cooperatiu estudiat.

Expectations, Learning and Monetary Policy: An Overview of Recent Research

Expectations about the future are central for determination of current macroeconomic outcomes and the formulation of monetary policy. Recent literature has explored ways for supplementing the benchmark of rational expectations with explicit models of expectations formation that rely on econometric learning. Some apparently natural policy rules turn out to imply expectational instability of private agents’ learning. We use the standard New Keynesian model to illustrate this problem and survey the key results about interest-rate rules that deliver both uniqueness and stability of equilibrium under econometric learning. We then consider some practical concerns such as measurement errors in private expectations, observability of variables and learning of structural parameters required for policy. We also discuss some recent applications including policy design under perpetual learning, estimated models with learning, recurrent hyperinflations, and macroeconomic policy to combat liquidity traps and deflation.

Testing the TASP: An Experimental Investigation of Learning in Games with Unstable Equilibria

We report experiments designed to test between Nash equilibria that are stable and unstable under learning. The “TASP” (Time Average of the Shapley Polygon) gives a precise prediction about what happens when there is divergence from equilibrium under fictitious play like learning processes. We use two 4 x 4 games each with a unique mixed Nash equilibrium; one is stable and one is unstable under learning. Both games are versions of Rock-Paper-Scissors with the addition of a fourth strategy, Dumb. Nash equilibrium places a weight of 1/2 on Dumb in both games, but the TASP places no weight on Dumb when the equilibrium is unstable. We also vary the level of monetary payoffs with higher payoffs predicted to increase instability. We find that the high payoff unstable treatment differs from the others. Frequency of Dumb is lower and play is further from Nash than in the other treatments. That is, we find support for the comparative statics prediction of learning theory, although the frequency of Dumb is substantially greater than zero in the unstable treatments.

A behavioral study of alpha-1b adrenergic receptor knockout mice: increased reaction to novelty and selectively reduced learning capacities.

Knockout mice lacking the alpha-1b adrenergic receptor were tested in behavioral experiments. Reaction to novelty was first assessed in a simple test in which the time taken by the knockout mice and their littermate controls to enter a second compartment was compared. Then the mice were tested in an open field to which unknown objects were subsequently added. Special novelty was introduced by moving one of the familiar objects to another location in the open field. Spatial behavior and memory were further studied in a homing board test, and in the water maze. The alpha-1b knockout mice showed an enhanced reactivity to new situations. They were faster to enter the new environment, covered longer paths in the open field, and spent more time exploring the new objects. They reacted like controls to modification inducing spatial novelty. In the homing board test, both the knockout mice and the control mice seemed to use a combination of distant visual and proximal olfactory cues, showing place preference only if the two types of cues were redundant. In the water maze the alpha-1b knockout mice were unable to learn the task, which was confirmed in a probe trial without platform. They were perfectly able, however, to escape in a visible platform procedure. These results confirm previous findings showing that the noradrenergic pathway is important for the modulation of behaviors such as reaction to novelty and exploration, and suggest that this is mediated, at least partly, through the alpha-1b adrenergic receptors. The lack of alpha-1b adrenergic receptors in spatial orientation does not seem important in cue-rich tasks but may interfere with orientation in situations providing distant cues only.

Learning about Risk and Return: A Simple Model of Bubbles and Crashes

This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock’s return. Recursive updating of both the conditional variance and the expected return implies several mechanisms through which learning impacts stock prices. Extended periods of excess volatility, bubbles and crashes arise with a frequency that depends on the extent to which past data is discounted. A central role is played by changes over time in agents’ estimates of risk.

Improving Students’ Learning Aspirations Beyond Post-Primary Education: A First Account of Two Non-Formal Education Programmes in Middle-Income

Non-formal education programmes are active in a number of developing countries. These programmes offer vulnerable students an opportunity to pursue their education although they were excluded for various reasons from the formal education systems. This paper examines the impact of two programmes (one in Mauritius, and one in Thailand) on their participants’ aspirations towards learning. We develop a methodology to measure the perception of students regarding their learning experience. More than a third of them, for example, believe that there is no barrier to their education. Most acknowledge the role of their teachers in raising their aspirations towards their educational achievement. When compared to male students, female students seem to value more the role of their education.

Non-cooperative incentives to share knowledge in competitive environments

In this paper we study a model where non-cooperative agents may exchange knowledge in a competitive environment. As a potential factor that could induce the knowledge disclosure between humans we consider the timing of the moves of players. We develop a simple model of a multistage game in which there are only three players and competition takes place only within two stages. Players can share their private knowledge with their opponents and the knowledge is modelled as in uencing their marginal cost of e¤ort. We identify two main mechanisms that work towards knowledge disclosure. One of them is that before the actual competition starts, the stronger player of the rst stage of a game may have desire to share his knowledge with the "observer", be- cause this reduces the valuation of the prize of the weaker player of that stage and as a result his e¤ort level and probability of winning in a ght. Another mechanism is that the "observer" may have sometimes desire to share knowledge with the weaker player of the rst stage, because in this way, by increasing his probability of winning in that stage, he decreases the probability of winning of the stronger player. As a result, in the second stage the "observer" may have greater chances to meet the weaker player rather than the stronger one. Keywords: knowledge sharing, strategic knowledge disclosure, multistage contest game, non-cooperative games

Notes on Agents’ Behavioral Rules Under Adaptive Learning and Studies of Monetary Policy

These notes try to clarify some discussions on the formulation of individual intertemporal behavior under adaptive learning in representative agent models. First, we discuss two suggested approaches and related issues in the context of a simple consumption-saving model. Second, we show that the analysis of learning in the NewKeynesian monetary policy model based on “Euler equations” provides a consistent and valid approach.

Fiscal Policy and Learning

Using the standard real business cycle model with lump-sum taxes, we analyze the impact of fiscal policy when agents form expectations using adaptive learning rather than rational expectations (RE). The output multipliers for government purchases are significantly higher under learning, and fall within empirical bounds reported in the literature (in sharp contrast to the implausibly low values under RE). Effectiveness of fiscal policy is demonstrated during times of economic stress like the recent Great Recession. Finally it is shown how learning can lead to dynamics empirically documented during episodes of 'fiscal consolidations.'

Baysian Model Averaging, Learning and Model Selection

Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

Finite Horizon Learning

Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the literature: Eulerequation learning, which assumes that consumption decisions are made to satisfy the one-step-ahead perceived Euler equation; and infinite-horizon learning, in which consumption today is determined optimally from an infinite-horizon optimization problem with given beliefs. In our approach, agents hold a finite forecasting/planning horizon. We find for the Ramsey model that the unique rational expectations equilibrium is E-stable at all horizons. However, transitional dynamics can differ significantly depending upon the horizon.

Anticipation, Learning and Welfare: the Case of Distortionary Taxation

We study the impact of anticipated fiscal policy changes in a Ramsey economy where agents form long-horizon expectations using adaptive learning. We extend the existing framework by introducing distortionary taxes as well as elastic labour supply, which makes agents. decisions non-predetermined but more realistic. We detect that the dynamic responses to anticipated tax changes under learning have oscillatory behaviour that can be interpreted as self-fulfilling waves of optimism and pessimism emerging from systematic forecast errors. Moreover, we demonstrate that these waves can have important implications for the welfare consequences of .scal reforms. (JEL: E32, E62, D84)

Unilateral Emissions Mitigation, Spillovers, and Global Learning

What's the role of unilateral measures in global climate change mitigation in a post-Durban, post 2012 global policy regime? We argue that under conditions of preference heterogeneity, unilateral emissions mitigation at a subnational level may exist even when a nation is unwilling to commit to emission cuts. As the fraction of individuals unilaterally cutting emissions in a global strongly connected network of countries evolves over time, learning the costs of cutting emissions can result in the adoption of such activities globally and we establish that this will indeed happen under certain assumptions. We analyze the features of a policy proposal that could accelerate convergence to a low carbon world in the presence of global learning.

What is the Causal Effect of Information and Learning about a Public Good on Willingness to Pay?

In this study we elicit agents’ prior information set regarding a public good, exogenously give information treatments to survey respondents and subsequently elicit willingness to pay for the good and posterior information sets. The design of this field experiment allows us to perform theoretically motivated hypothesis testing between different updating rules: non-informative updating, Bayesian updating, and incomplete updating. We find causal evidence that agents imperfectly update their information sets. We also field causal evidence that the amount of additional information provided to subjects relative to their pre-existing information levels can affect stated WTP in ways consistent overload from too much learning. This result raises important (though familiar) issues for the use of stated preference methods in policy analysis.

Non-Cooperative Asymptotic Oligopoly in Economies with Infinitely Many Commodities

In this paper, we extend the non-cooperative analysis of oligopoly to exchange economics with infinitely many commodities by using strategic market games. This setting can be interpreted as a model of oligopoly with differentiated commodities by using the Hotelling line. We prove the existence of an "active" Cournot-Nash equilibrium and show that, when traders are replicated, the price vector and the allocation converge to the Walras equilibrium. We examine how the notion of oligopoly extends to our setting with a countable infinity of commodities by distinguishing between asymptotic oligopolists and asymptotic price-takes. We illustrate these notions via a number of examples.