Modeling in Forestry Using Mixture Models Fitted to Grouped and Ungrouped Data

The creation and maintenance of complex forest structures has become an important forestry objective. Complex forest structures, often expressed in multimodal shapes of tree size/diameter (DBH) distributions, are challenging to model. Mixture probability density functions of two- or three-component gamma, log-normal, and Weibull mixture models offer a solution and can additionally provide insights into forest dynamics. Model parameters can be efficiently estimated with the maximum likelihood (ML) approach using iterative methods such as the Newton-Raphson (NR) algorithm. However, the NR algorithm is sensitive to the choice of initial values and does not always converge. As an alternative, we explored the use of the iterative expectation-maximization (EM) algorithm for estimating parameters of the aforementioned mixture models because it always converges to ML estimators. Since forestry data frequently occur both in grouped (classified) and ungrouped (raw) forms, the EM algorithm was applied to explore the goodness-of-fit of the gamma, log-normal, and Weibull mixture distributions in three sample plots that exhibited irregular, multimodal, highly skewed, and heavy-tailed DBH distributions where some size classes were empty. The EM-based goodness-of-fit was further compared against a nonparametric kernel-based density estimation (NK) model and the recently popularized gamma-shaped mixture (GSM) models using the ungrouped data. In this example application, the EM algorithm provided well-fitting two- or three-component mixture models for all three model families. The number of components of the best-fitting models differed among the three sample plots (but not among model families) and the mixture models of the log-normal and gamma families provided a better fit than the Weibull distribution for grouped and ungrouped data. For ungrouped data, both log-normal and gamma mixture distributions outperformed the GSM model and, with the exception of the multimodal diameter distribution, also the NK model. The EM algorithm appears to be a promising tool for modeling complex forest structures.

Landscape Models of Complex Change

This chapter reviews recent advances in the NK modeling literature conceptualizing organizational change and innovation as a search over a complex landscape. It discusses both strengths and limitations of this perspective and delineates potential for future research directions. The key argument is that the NK model in its traditional form may be exhausting the theoretical insights that it can provide to the field. However, substantial modifications and extensions of the NK model or new classes of landscape models may provide fresh perspectives. Specifically, we consider the modeling efforts that endogenize the landscape construction as the next frontier in this literature. We also discuss several recent studies that incorporate various extensions of the NK model and allow for agent-driven changes to the landscape.

New Evolutionary Theory Via Simulation

Simulation modelling is very common in evolutionary approaches to economics, strategy, and technological innovation. A well-established simulation framework is the NK model of fitness landscapes, which is particularly useful for modelling the processes of technological adaptation, whose difficulty is reflected into how a fitness landscape behaves as a function of the number of components and internal interdependencies of a technology. However, classical NK models become problematic when modelling different types of processes, such as technological exaptation, unless a broader family of NK models is considered. After reviewing the classical NK model, this chapter explores the potential of ‘generalized’ NK landscapes, followed by a review of other important simulation frameworks in evolutionary theory, such as holey landscapes, quantum-like approaches, and history-friendly models.

Toward a Fitness Landscape Model of Firms' IT-Enabled Dynamic Capabilities

This chapter presents, extends, and integrates a complexity science perspective and applies this to the firm's IT-enabled dynamic capabilities (ITDCs). By doing so, this chapter leverages statistical survey data and uses them as parameters for a simulation using Kauffman's NK-model. This NK-model creates stochastically generated fitness landscapes that are parameterized using a finite number of ‘N' elements, or capabilities, and ‘K' complex interactions between those capabilities, and studies the performance (fitness) of systems. We simulate a firm's effort to adaptively explore and walk through a fitness landscape of possible strategies of inter-related capabilities to reach toward higher levels of fitness of ITDCs. Also, our fitness landscape model provides realistic scenarios with a nexus of possible business strategies that can be employed considering a firm's current status, interdependency, and alignment among its capabilities. Our work suggests that firms achieve the highest fitness values when the interdependency among the individual capabilities is relatively small.

Delegating Optimal Monetary Policy Inertia in a Small-Open Economy

This paper explores the delegation of several targeting regimes in a small open new Keynesian (NK) model and examines how central banks overcome stabilization bias in a small open NK model. Results indicate that both speed limit and real exchange rate targeting can carry the isomorphic properties of optimal monetary policy over to the closed economy. In addition, neither nominal income growth targeting nor CPI inflation targeting replicates a commitment policy. These findings provide new implications for optimal monetary policy in an open economy.

Monetary and Macroprudential Policy with Endogenous Risk

We extend the New Keynesian (NK) model to include endogenous risk. Lower interest rates not only shift consumption intertemporally but also conditional output risk via their impact on risk-taking, giving rise to a vulnerability channel of monetary policy. The model fits the conditional output gap distribution and can account for medium-term increases in downside risks when financial conditions are loose. The policy prescriptions are very different from those in the standard NK model: monetary policy that focuses purely on inflation and output-gap stabilization can lead to instability. Macroprudential measures can mitigate the intertemporal risk-return tradeoff created by the vulnerability channel.