On a special case of non-symmetric resource extraction games with unbounded payoffs

The game of resource extraction/capital accumulation is a stochastic infinite-horizon game, which models a joint utilization of a productive asset over time. The paper complements the available results on pure Markov perfect equilibrium existence in the non-symmetric game setting with an arbitrary number of agents. Moreover, we allow that the players have unbounded utilities and relax the assumption that the stochastic kernels of the transition probability must depend only on the amount of resource before consumption. This class of the game has not been examined beforehand. However, we could prove the Markov perfect equilibrium existence only in the specific case of interest. Namely, when the players have constant relative risk aversion (CRRA) power utilities and the transition law follows a geometric random walk in relation to the joint investment. The setup with the chosen characteristics is motivated by economic considerations, which makes it relevant to a certain range of real-word problems.

Time Series Analysis of Forest Dynamics at the Ecoregion Level

Forecasting of forest dynamics at a large scale is essential for land use management, global climate change and biogeochemistry modeling. We develop time series models of the forest dynamics in the conterminous United States based on forest inventory data collected by the US Forest Service over several decades. We fulfilled autoregressive analysis of the basal forest area at the level of US ecological regions. In each USA ecological region, we modeled basal area dynamics on individual forest inventory pots and performed analysis of its yearly averages. The last task involved Bayesian techniques to treat irregular data. In the absolute majority of ecological regions, basal area yearly averages behave as geometric random walk with normal increments. In California Coastal Province, geometric random walk with normal increments adequately describes dynamics of both basal area yearly averages and basal area on individual forest plots. Regarding all the rest of the USA’s ecological regions, basal areas on individual forest patches behave as random walks with heavy tails. The Bayesian approach allowed us to evaluate forest growth rate within each USA ecological region. We have also implemented time series ARIMA models for annual averages basal area in every USA ecological region. The developed models account for stochastic effects of environmental disturbances and allow one to forecast forest dynamics.

Autoregressive Modeling of Forest Dynamics

In this work, we employ autoregressive models developed in financial engineering for modeling of forest dynamics. Autoregressive models have some theoretical advantage over currently employed forest modeling approaches such as Markov chains and individual-based models, as autoregressive models are both analytically tractable and operate with continuous state space. We performed a time series statistical analysis of forest biomass and basal areas recorded in Quebec provincial forest inventories from 1970 to 2007. The geometric random walk model adequately describes the yearly average dynamics. For individual patches, we fit an autoregressive process (AR) of order 1 capable to model negative feedback (mean-reversion). Overall, the best fit also turned out to be geometric random walk; however, the normality tests for residuals failed. In contrast, yearly means were adequately described by normal fluctuations, with annual growth on average of 2.3%, but with a standard deviation of order of 40%. We used a Bayesian analysis to account for the uneven number of observations per year. This work demonstrates that autoregressive models represent a valuable tool for the modeling of forest dynamics. In particular, they quantify the stochastic effects of environmental disturbances and develop predictive empirical models on short and intermediate temporal scales.

Random-walk dynamics of exploited fish populations

Niwa, H-S. 2007. Random-walk dynamics of exploited fish populations. – ICES Journal of Marine Science, 64: 496–502. Fished populations have been heavily fished over a wide range of stock sizes, and the data for such stocks are potentially of great interest. Population variability in stock histories has focused attention on the predictability of conditions of sustainability when harvesting fish. Here, I examine empirically the time-series data on 27 commercial fish stocks in the North Atlantic. The variability in population growth rate (i.e. the annual changes in the logarithms of population abundance) is described by a Gaussian distribution. The signs (up or down) of successive changes in the population trajectory are independent, as if determined by the toss of a coin. The process of population variability therefore corresponds to a geometric random walk.