Adaptive learning in an asymmetric auction: genetic algorithm approach

Agent-based simulations are performed to study adaptive learning in the context of asymmetric first-price auctions. Non-linearity of the Nash equilibrium strategies is used to investigate the effect of task complexity on adaptive learning by varying the degree of approximation the agents can handle. In addition, learning in different information environments is explored. Social learning allows agents to imitate each other’s bidding strategies based on their relative success. Under individual learning agents are limited to their own experience. We observe convergence to steady states near the predicted equilibrium in all cases. The ability to learn non-linear functions helps the agents with a non-linear equilibrium strategy but hurts the agents with an almost linear one. Better information about the opponent population has a relatively modest impact. A larger number of strategies to experiment with and an ability to systematically compare strategies by holding a number of factors constant have a comparatively stronger beneficial effect.     

Optimal consumption under uncertainty,liquidity constraints,and bounded rationality

I study how boundedly rational agents can learn a “good” solution to an infinite horizon optimal consumption problem under uncertainty and liquidity constraints. Using an empirically plausible theory of learning I propose a class of adaptive learning algorithms that agents might use to choose a consumption rule. I show that the algorithm always has a globally asymptotically stable consumption rule, which is optimal. Additionally, I present extensions of the model to finite horizon settings, where agents have finite lives and life-cycle income patterns. This provides a simple and parsimonious model of consumption for large agent based models.     

An Econometric Analysis of Polish Inflation Dynamics with Learning about Rational Expectations

Rational expectations modelling has been criticized for assuming that economic agents can learn quickly about and compute rational price expectations. In response, various authors have studied theoretical models in which economic agents use adaptive statistical rules to develop price expectations. A goal of this literature has been to compare resulting learning equilibria with rational expectations equilibria. The lack of empirical analysis in this literature suggests that adaptive learning makes otherwise linear dynamic models nonlinearly intractable for current econometric technology. In response to the lack of empirical work in this literature, this paper applies to post-1989 monthly data for Poland a new method for modelling learning about price expectations. The key idea of the method is to modify Cagan’s backward-looking adaptive-expectations hypothesis about the way expectations are actually updated to a forward-looking characterization which instead specifies the result of learning. It says that, whatever the details of how learning actually takes places, price expectations are expected to converge geometrically to rationality. The method is tractable because it involves linear dynamics. The paper contributes substantively by analyzing the recent Polish inflation, theoretically by characterizing learning, and econometrically by using learning as a restriction for identifying (i.e., estimating wth finite variance) unobserved price expectations with the Kalman filter. This revised version was published online in July 2006 with corrections to the Cover Date.     

Learning in an estimated medium-scale DSGE model

We evaluate the empirical relevance of learning by private agents in an estimated medium-scale DSGE model. We replace the standard rational expectations assumption in the Smets and Wouters (2007) model by a constant-gain learning mechanism. If agents know the correct structure of the model and only learn about the parameters, both expectation mechanisms produce very similar results, and only the transition dynamics that are generated by specific initial beliefs seem to improve the fit. If, instead, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and, depending on the specification of the initial beliefs, the marginal likelihood of the model can improve significantly. These best-fitting models add additional persistence to the dynamics and this reduces the gap between the IRFs of the DSGE model and the more data-driven DSGE-VAR model. However, the learning dynamics do not systematically alter the estimated structural parameters related to the nominal and real frictions in the DSGE model.     

A comparison of endogenous and exogenous timing in a social learning experiment

We experimentally investigate social learning in a two-agent prediction game with both exogenous and endogenous ordering of decisions on a continuous action space. We are first in comparing exogenous and endogenous ordering within one framework, which enables a direct comparison of both structures in terms of informational efficiency, strategic delay and welfare. More efficient observational learning leads to more accurate predictions in the endogenous setting and increases informational efficiency compared to an exogenous setting. However, strategic delay induces waiting costs that offset these benefits and lead to a parity of exogenous and endogenous ordering in terms of welfare results. Our results hold relevance for the efficient design of decision regimes in contexts characterized by continuous action spaces.     

Team Formation and Self-serving Biases

There is extensive evidence which indicates that people learn positively about themselves. We build on this finding to develop a model of team formation. We show that under complete information learning positively about oneself prevents efficient team formation. Agents becoming overconfident tend to ask for an excessive share of the group outcome. Positive learning generates divergence in workers' beliefs and hampers efficient team formation. Interestingly, in a context of incomplete information regarding the partner's ability, extensive learning biases may reduce the divergence in agents' beliefs and facilitate efficient team formation as a result. We apply our model to coauthorship and organizational issues.     

Learning competitive pricing strategies by multi-agent reinforcement learning

In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to unforeseen results and increasingly dynamic behavior of the market. In this article we shed some light on price developments arising from a simple price adaptation strategy. Furthermore, we examine several adaptive pricing strategies and their learning behavior in a co-learning scenario with different levels of competition. Q-learning manages to learn best-reply strategies well, but is expensive to train.     

Adaptive learning and monetary exchange

This paper investigates three classic questions in monetary theory: How can an intrinsically worthless asset, such as fiat money, maintain value as a medium of exchange? What are the short-run and long-run effects of a change in the money supply? What is the social cost of inflation? I answer these questions using a microfounded model of monetary exchange that replaces the rational expectations assumption with an adaptive learning rule. First, I show that monetary exchange is a robust arrangement in the sense that agents are able to learn the stationary monetary equilibrium while the non-monetary equilibrium is unstable under learning. Second, an unanticipated monetary injection has real effects in the short-run because learning the value of money takes time. In the long run, agents successfully learn the value of money, hence money is neutral. Third, under a constant money growth policy, an increase in the growth rate of money increases output in the short-run producing a short-run Phillips curve. A ten percent increase in the money growth rate has a social cost of 0.41 percent of output per year. Alternatively, a ten percent decrease in the money growth rate has a social benefit of 0.37 percent of output per year.     

Linear learning in changing environments

The decision maker receives signals imperfectly correlated with an unobservable state variable and must take actions whose payoffs depend on the state. The state randomly changes over time. In this environment, we examine the performance of simple linear updating rules relative to Bayesian learning. We show that a range of parameters exists for which linear learning results in exactly the same decisions as Bayesian learning, although not in the same beliefs. Outside this parameter range, we use simulations to demonstrate that the consumption level attainable under the optimal linear rule is virtually indistinguishable from the one attainable under Bayes’ rule, although the respective decisions will not always be identical. These results suggest that simple rules of thumb can have an advantage over Bayesian updating when more complex calculations are more costly to perform than less complex ones. We demonstrate the implications of such an advantage in an evolutionary model where agents “learn to learn.”     

Knowledge-oriented human resource configurations,the new product development learning process,and perceived new product performance

We propose a set of organizational efforts that can help companies accumulate and learn knowledge related to new product development (NPD) activities. We call it the NPD learning process and argue that a set of coherent human resource management (HRM) practices, termed knowledge-oriented human resource (HR) configuration, can facilitate the NPD learning process. Collecting survey data from Taiwan, we find that the knowledge-oriented HR configuration is positively related to the NPD learning process and that the NPD learning process is positively related to managers' perceived new product performance. This study contributes to the literature of strategic HRM and innovation management.     

Disseminating knowledge to build a learning organization

In recent years the learning organization has become popular in the management literature but the extent to which staff typically obtain access to the information they need for enhanced learning is not well understood. This paper examines the access to information experienced by staff within a New Zealand company in terms of the topics on which information is received and the sources from which information comes. The results show significant divisions within the company on status grounds for information that is currently received. Nevertheless, no such divisions were found for the information that is sought. Limitations appeared more obviously with regard to formal information sources (for which the company is responsible) than for the informal sources (which the individual finds it easier to access). Implications for companies aspiring to strengthen their capacity to learn are briefly discussed.     

Competitive prizes: when less scrutiny induces more effort

We consider a principal who is keen to induce his agents to work at their maximal effort levels. To this end, he samples n days at random out of the T days on which they work, and awards a prize of B dollars to the most productive agent. The principal’s policy (B, n) induces a strategic game Γ(B, n) between the agents. We show that to implement maximal effort levels weakly (or, strongly) as a strategic equilibrium (or, as dominant strategies) in Γ(B, n), at the least cost B to himself, the principal must choose a small sample size n. Thus less scrutiny by the principal induces more effort from the agents.The need for reduced scrutiny becomes more pronounced when agents have information of the history of past plays in the game. There is an inverse relation between information and optimal sample size. As agents acquire more information (about each other), the principal, so to speak, must “undo” this by reducing his information (about them) and choosing the sample size n even smaller.     

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.     

Why do firms set environmental performance goals?: Some evidence from organizational theory

Organizational goal setting is considered a critical strategic first step for corporations as it provides the basis for developing a roadmap for organizational activity as well as guidance for establishing the metrics to measure progress. Yet despite significant research interest in the environmental performance of corporations, environmental goal setting has received little attention. For example, it is not known why firms set environmental goals. Understanding this goal setting behavior is necessary to develop mechanisms to improve organizations' environmental management and performance. This study uses organizational change models of institutionalism, stakeholder management, natural selection, strategic choice and organizational learning to examine why firms set environmental performance goals. First, propositions related to environmental goal setting are developed from the models. The goal setting propositions use the goals of the US EPA's 33/50 programme, a national voluntary pollution prevention effort which aimed for a 33% reduction in releases by 1993 and a 50% reduction by 1995, as a basis for comparison to individual company goal setting. Next, the toxic release reduction goals of the 118 US corporations who set goals are analysed to determine which organizational change model propositions they support. All five models of organizational change examined here–institutionalism, stakeholder management, natural selection, strategic choice and organizational learning–show some promise in explaining corporate environmental goal setting. The combination of these models leads to the following depiction of the motivation for toxic release reduction. Firms will set goals to reduce toxic releases in an effort to respond to regulators and other factors in the institutional and stakeholder environment. This goal setting is likely to be enhanced if it can be more directly tied to economic benefits such as cost savings or if it is chosen by natural selection. This in turn will promote organizational learning with the end result of better environmental and economic performance. These findings provide some empirical evidence on which to base strategies for improving corporate environmental management. Copyright © 1999 John Wiley & Sons, Ltd and ERP Environment.     

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 labor supply, which makes agents’ decisions non-predetermined but more realistic. We detect that the dynamic responses to anticipated tax changes under learning have oscillatory behavior 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 fiscal reforms.     

Dynamic competitive retail pricing behavior with uncertainty and learning

When competing retailers lack full information about rivals' decision processes, how will dynamic pricing behavior vary from patterns observed in more traditional static or full-information models? We investigate this question in a dynamic alternating-moves duopoly model. Retailers update (linear) conjectures about rivals' future prices in a Bayesian fashion. We show that as observed and expected prices converge, a pricing equilibrium is always achieved, whether or not the conjectured and actual values of the slope of the rival's best response function are consistent. Assuming specific parameter values, we compare equilibrium prices and associated profits in our Bayesian learning model with those obtained under the assumptions of static Nash behavior, collusive behavior and dynamically optimal behavior with full information. We apply the notions of strategic substitutability and strategic complementarity to the analysis and find that when products are strategic complements, conjectures of higher rival price responsiveness lead to higher steady-state prices and profits. The reverse is true for strategic substitutes. We also find that learning about a rival's behavior proceeds more quickly, the less intensely related in demand are products. We find, in general, that equilibrium pricing patterns and profits can vary considerably from those in full-information environments, but that even with grossly wrong beliefs about rival behavior, competing retailers are still attracted to an equilibrium. The analysis suggests not only the value of investigating lessthan-full information situations but also the potential incremental value of signalling greater or less aggressiveness than truly characterizes one's behavior as a strategic option.     

Government debt,learning and the term structure

Does a change in the public׳s holdings of government debt affect the term structure of interest rates? Empirical analysis using a VAR model indicates that a rise in these holdings of the real debt-to-GDP ratio increases both the three-month and ten-year U.S. nominal yields in a statistically significant manner. The maturity composition of debt is also found to matter: innovations in holdings of long-term debt affect the term structure, while increases in short-term debt affect inflation expectations. These effects of changes in holdings of debt on the yield curve can be derived in a general equilibrium model in which the government issues exponentially-maturing riskless debt, financed by lump-sum taxes, and the optimizing agents are adaptive learners. On calibrating the average maturity of debt in the model to match that of U.S. Treasury debt since the 1980s, I find that positive innovations in government debt lead to increases in asset yields. This is because agents do not learn the principle of Ricardian equivalence exactly, and perceive increases in holdings of government bonds as a rise in their net wealth. Imposing rational expectations on the agents eliminates this channel, and changes in holdings of government debt have no effect on yields. The learning model also implies that as the real debt-to-GDP ratio increases, and the average maturity of debt becomes longer, the agents become less likely to learn that Ricardian equivalence holds.     

Flexibility in Strategic Decision Making: Informational and Ideological Perspectives

Adaptation is a crucial challenge for organizations, and an important theme in the strategy and organization theory literature. We still have much to learn, however, about the strategic processes by which adaptation is achieved. In this paper we focus on a basic element in the adaptation process, i.e. flexibility within the strategic decision-making process. We concentrate on strategic decisions because these choices are the most important adaptations the firm makes. We suggest that the core of all organizational adaptation is a decision-making process. Unless the decision-making process itself is flexible, it is unlikely the organization can be flexible enough to adapt. We derive hypotheses concerning the factors that lead to flexibility (versus rigidity) from both information processing and ideological perspectives, and test them in a study involving 57 strategic decisions in 25 companies. Our results identify three contextual factors related to both perspectives -- including competitive threat, slack and uncertainty -- that are helpful in understanding flexibility in strategy decision making. While managers appear to be more flexible when decisions are uncertain, we found that in the very conditions where managers need the most flexibility (high competitive threat and low slack), they are least likely to be flexible.     

The implications of financial frictions and imperfect knowledge in the estimated DSGE model of the U.S. economy

In this paper, I study how alternative assumptions about expectation formation can modify the implications of financial frictions for the real economy. I incorporate a financial accelerator mechanism into a version of the Smets and Wouters (2007) DSGE framework and explore the properties of the model assuming, on the one hand, complete rationality of expectations and, alternatively, several learning algorithms that differ in terms of the information set used by agents to produce the forecasts. I show that the implications of the financial accelerator for the business cycle may vary depending on the approach to modeling the expectations. The results suggest that the learning scheme based on small forecasting functions is able to amplify the effects of financial frictions relative to the model with Rational Expectations. Specifically, I show that the dynamics of real variables under learning is driven to a significant extent by the time variation of agents’ beliefs about financial sector variables. During periods when agents perceive asset prices as being relatively more persistent, financial shocks lead to more pronounced macroeconomic outcomes. The amplification effect rises as financial frictions become more severe. At the same time, a learning specification in which agents use more information to generate predictions produces very different asset price and investment dynamics. In such a framework, learning cannot significantly alter the real effects of financial frictions implied by the Rational Expectations model.     

Trading off learning and performance: Exploration and exploitation at work

Employees are increasingly given control over how they learn, and their choices for training are diverse and varied, yet employees must balance competing demands. On one hand, they are expected to be increasingly efficient in their current job duties – on the other hand, they are expected to develop new skills and competencies that enable them to adapt and respond to changing job demands. Drawing from the organizational learning literature, we propose a model of worker and work characteristics that inform choices between two mindsets related to learning at work. The first mindset is exploration, or the pursuit of learning outside one’s current knowledge domain; the second mindset is exploitation, the refinement/deepening of one’s existing knowledge stock focusing on the task at hand. We further propose that these strategic choices, or trade-offs, influence employee learning and performance in unique ways, with different implications for both routine and adaptive performance. Finally, we incorporate the notions of feedback loops and risk assessments that influence ongoing decisions between exploration and exploitation mindsets. Recommendations for future research and extensions of the theoretical model are also proposed.