Sequential decision problems, dependent types and generic solutions

We present a computer-checked generic implementation for solving finite horizon sequential decision problems. This is a wide class of problems, including intertemporal optimizations, knapsack, optimal bracketing, scheduling, etc. The implementation can handle time-step dependent control and state spaces, and monadic representations of uncertainty (such as stochastic, non-deterministic, fuzzy, or combinations thereof). This level of genericity is achievable in a programming language with dependent types (we have used both Idris and Agda). Dependent types are also the means that allow us to obtain a formalization and computer-checked proof of the central component of our implementation: Bellman’s principle of optimality and the associated backwards induction algorithm. The formalization clarifies certain aspects of backwards induction and, by making explicit notions such as viability and reachability, can serve as a starting point for a theory of controllability of monadic dynamical systems, commonly encountered in, e.g., climate impact research.Publisher PDFPeer reviewe

Assessing farmer behaviour as affected by policy and technological innovations: bio-economic farm models

Farm Management,

Sequential Design for Optimal Stopping Problems

We propose a new approach to solve optimal stopping problems via simulation. Working within the backward dynamic programming/Snell envelope framework, we augment the methodology of Longstaff-Schwartz that focuses on approximating the stopping strategy. Namely, we introduce adaptive generation of the stochastic grids anchoring the simulated sample paths of the underlying state process. This allows for active learning of the classifiers partitioning the state space into the continuation and stopping regions. To this end, we examine sequential design schemes that adaptively place new design points close to the stopping boundaries. We then discuss dynamic regression algorithms that can implement such recursive estimation and local refinement of the classifiers. The new algorithm is illustrated with a variety of numerical experiments, showing that an order of magnitude savings in terms of design size can be achieved. We also compare with existing benchmarks in the context of pricing multi-dimensional Bermudan options.Comment: 24 page

Application of multiobjective genetic programming to the design of robot failure recognition systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge

Managing design variety, process variety and engineering change: a case study of two capital good firms

Many capital good firms deliver products that are not strictly one-off, but instead share a certain degree of similarity with other deliveries. In the delivery of the product, they aim to balance stability and variety in their product design and processes. The issue of engineering change plays an important in how they manage to do so. Our aim is to gain more understanding into how capital good firms manage engineering change, design variety and process variety, and into the role of the product delivery strategies they thereby use. Product delivery strategies are defined as the type of engineering work that is done independent of an order and the specification freedom the customer has in the remaining part of the design. Based on the within-case and cross-case analysis of two capital good firms several mechanisms for managing engineering change, design variety and process variety are distilled. It was found that there exist different ways of (1) managing generic design information, (2) isolating large engineering changes, (3) managing process variety, (4) designing and executing engineering change processes. Together with different product delivery strategies these mechanisms can be placed within an archetypes framework of engineering change management. On one side of the spectrum capital good firms operate according to open product delivery strategies, have some practices in place to investigate design reuse potential, isolate discontinuous engineering changes into the first deliveries of the product, employ ‘probe and learn’ process management principles in order to allow evolving insights to be accurately executed and have informal engineering change processes. On the other side of the spectrum capital good firms operate according to a closed product delivery strategy, focus on prevention of engineering changes based on design standards, need no isolation mechanisms for discontinuous engineering changes, have formal process management practices in place and make use of closed and formal engineering change procedures. The framework should help managers to (1) analyze existing configurations of product delivery strategies, product and process designs and engineering change management and (2) reconfigure any of these elements according to a ‘misfit’ derived from the framework. Since this is one of the few in-depth empirical studies into engineering change management in the capital good sector, our work adds to the understanding on the various ways in which engineering change can be dealt with