Markets for digital information goods provide the possibility of exploring new and more complex pricing schemes, due to information goods' flexibility and negligible marginal cost. In this paper we compare the dynamic performance of price schedules of varying complexity under two different...

Distributed clusters like the Grid and PlanetLab enable the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. One major challenge is allocating resources in an economically efficient and low-latency way. A common solution is proportional share,...

We have developed an agent-based computational model, extension of an analytical model1 that studies the structure of coalitions of B-to-C web sites, when Internet buyers incur search costs for finding the good that matches their preferences, and coalitions of sites reduce this cost through...

While under recursive least squares learning the dynamics of the economy converges to rational expectations equilibria (REE) which are E–stable, some recent examples propose that E–stability is not a sufficient condition for learnability. In this paper, we provide some further...

We examine optimal policy in an open-economy model with uncertainty and learning, where monetary policy actions affect the economy through the real exchange rate channel. Our results show that the degree of caution or activism in optimal policy depends on whether central banks are in coordinated...

We provide sufficient conditions and necessary conditions for stability of an economy under structural mixed recursive least squares/stochastic gradient heterogeneous learning of agents with possibly different degrees of inertia. We have found a unifying condition which is sufficient for...

In this paper we perform an in—depth investigation of relative merits of two adaptive learning algorithms with constant gain, Recursive Least Squares (RLS) and Stochastic Gradient (SG), using the Phelps model of monetary policy as a testing ground. The behavior of the two learning...

In this paper we explore the information processing problem of the firm by modeling the firm as type of network, which is comprised of two kinds of agents, 'searchers' and 'managers.' The searchers explore the external environment and report the information to the managers. We explore the role...

I introduce a method to transform a T-map when agents form expectations using a misspecified learning mechanism inconsistent with a structural equation of a multivariate economic model. By transforming the perceived law of motion (PLM) into a the form of a Seemingly Unrelated Regression (SUR)...