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.

Entrepreneurial Orientation, Intangible Assets and Firm Growth: the impact of ‘Spirit and Material’ on the growth of Chinese private firms

This paper has three contributions. First, it shows how field work within small firms in PR Chinese has provided new evidence which enables us to measure and calibrate Entrepreneurial Orientation (EO), as ‘spirit’, and Intangible Assets (IA), as ‘material’, for use in models of small firm growth. Second, it uses inter-item correlation analysis and both exploratory and confirmatory factor analysis to provide new measures of EO and IA, in index and in vector form, for use in econometric models of firm growth. Third, it estimates two new econometric models of small firm employment growth in PR China, under the null hypothesis of Gibrat’s Law, using our two new index-based and vector-based measures of EO and IA. Estimation is by OLS with adjustment for heteroscedasticity, and for sample selectivity. Broadly, it finds that EO attributes have had little significant impact on small firm growth, and indeed innovativeness and pro-activity paradoxically may even dampen growth. However, IA attributes have had a positive and significant impact on growth, with networking, and technological knowledge being of prime importance, and intellectual property and human capital being of lesser but still significant importance. In the light of these results, Gibrat’s Law is generalized, and Jovanovic’s learning theory is extended, to emphasise the importance of IA to growth. These findings cast new empirical light on the oft-quoted national slogan in PR China of “spirit and material”. So far as small firms are concerned, this paper suggests that their contribution to PR China’s remarkable economic growth is not so much attributable to the ‘spirit’ of enterprise (as suggested by propaganda) as, more prosaically, to the pursuit of the ‘material’.

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.

The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Environmental Goods

This paper develop and estimates a model of demand estimation for environmental public goods which allows for consumers to learn about their preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two environmental goods. We find that the predictions of the theoretical exercise that additional experience makes consumers more certain over their preferences in both mean and variance are supported in each case.