Learning and transfer in dynamic decision environments

An important aspect of learning is the ability to transfer knowledge to new contexts. However, in dynamic decision tasks, such as bargaining, firefighting, and process control, where decision makers must make repeated decisions under time pressure and outcome feedback may relate to any of a number of decisions, such transfer has proven elusive. This paper proposes a two-stage connectionist model which hypothesizes that decision makers learn to identify categories of evidence requiring similar decisions as they perform in dynamic environments. The model suggests conditions under which decision makers will be able to use this ability to help them in novel situations. These predictions are compared against those of a one-stage decision model that does not learn evidence categories, as is common in many current theories of repeated decision making. Both models' predictions are then tested against the performance of decision makers in an Internet bargaining task. Both models correctly predict aspects of decision makers' learning under different interventions. The two-stage model provides closer fits to decision maker performance in a new, related bargaining task and accounts for important features of higher-performing decision makers' learning. Although frequently omitted in recent accounts of repeated decision making, the processes of evidence category formation described by the two-stage model appear critical in understanding the extent to which decision makers learn from feedback in dynamic tasks. Faison (Bud) Gibson is an Assistant Professor at College of Business, Eastern Michigan University. He has extensive experience developing and empirically testing models of decision behavior in dynamic decision environments.     

Metal complexation of crosslinked polyacrylamide-supported dithiocarbamates: Effect of the molecular character and extent of crosslinking on complexation

Dithiocarbamate functions were incorporated into different polyacrylamide matrices crosslinked with a flexible and hydrophilic crosslinking agent, tetraethyleneglycol diacrylate (TEGDA), and their complexation behaviours were investigated. Crosslinked polyacrylamides with varying extents of the tetrafunctional TEGDA crosslinks were prepared by free radical solution polymerization at 60°C using potassium persulphate as initiator in ethanol. The dithiocarbamate functionality was incorporated into these polyacrylamides by a two-step polymer-analogous reaction involving (i)trans-amidation with ethylenediamine and (ii) dithiocarbamylation of the aminopolyacrylamide with carbon disulphide and alkali. The complexations of dithiocarbamate with Cu(II), Ni(II), Zn(II), Co(II) and Hg(II) ions were followed under different conditions. The metal ion intake varied with the extent of the crosslinking agent and the observed trend in complexation is Hg(II) > Cu(II)> Zn(II)> Co(II)> Ni (II). The time-course of complexation, the possibility of recycling, swelling characteristics, and spectral and thermal analyses were carried out. The thermal stability increases upon complexation with metal ions.     

Reservoir operations optimization via fuzzy criterion decision processes

In this paper, we propose the treatment of complex reservoir operation problems via our newly developed tool of fuzzy criterion decision processes. This novel approach has been shown to be a more flexible and useful analysis tool especially when it is desirable to incorporate an expert’s knowledge into the decision models. Additionally, it has been demonstrated that this form of decision models will usually result in an optimal solution, which guarantees the highest satisfactory degree. We provide a practical exemplification procedure for the models presented as well as an application example.     

Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming

In this paper, we first refine a recently proposed metaheuristic called “Marriage in Honey-Bees Optimization” (MBO) for solving combinatorial optimization problems with some modifications to formally show that MBO converges to the global optimum value. We then adapt MBO into an algorithm called “Honey-Bees Policy Iteration” (HBPI) for solving infinite horizon-discounted cost stochastic dynamic programming problems and show that HBPI also converges to the optimal value.     

SPAR: stochastic programming with adversarial recourse

We consider a general adversarial stochastic optimization model. Our model involves the design of a system that an adversary may subsequently attempt to destroy or degrade. We introduce SPAR, which utilizes mixed-integer programming for the design decision and a Markov decision process (MDP) for the modeling of our adversarial phase.     

New reals: Can live with them,can live without them

We give a self‐contained proof of the preservation theorem for proper countable support iterations known as “tools‐preservation”, “Case A” or “first preservation theorem” in the literature. We do not assume that the forcings add reals. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Solving multiple-objective problems in the objective space

Projection and relaxation techniques are employed to decompose a multiobjective problem into a two-level structure. The basic manipulation consists in projecting the decision variables onto the space of the implicit tradeoffs, allowing the definition of a relaxed multiobjective master problem directly in the objective space. An additional subproblem tests the feasibility of the solution encountered by the relaxed problem. Some properties of the relaxed problem (linearity, small number of variables, etc.) render its solution efficient by a number of methods. Representatives of two different classes of multiobjective methods [the Geoffrion, Dyer, Feinberg (GDF) method and the fuzzy method of Baptistella and Ollero] are implemented and applied within this context to a water resources allocation problem. The results attest the computational viability of the overall procedure and its usefulness for the solution of multiobjective problems.This work was partially sponsored by grants from CNP_q and FAPESP, Brazil. The authors are indebted to the anonymous reviewers for their valuable comments.     

阴极支撑型固体氧化物燃料电池的制备与测试

采用固相反应法合成A缺位的(La_0.8Sr_0.2)_0.95MnO₃(LSM95)作为阴极材料,Zr_0.9Sc_0.1SO_1.95(SSZ)商业粉体作为电解质材料,溶胶-凝胶法合成的La_0.8Sr_0.2Cr_0.5Mn_0.5O₃-(LSCrM)作为阳极电催化材料,利用流延、共烧结及浸渍法得到结构为LSCrM-CeO₂|SSZ|3YSZ-LSM95的阴极支撑型固体氧化物燃料电池,分别在氢气气氛和甲烷气氛中进行电化学性能测试. 结果表明,浸渍0.11 g·cm ^-2 CeO₂的LSCrM-CeO₂|SSZ|3YSZ-LSM95单电池在以CH₄为燃料时,600、650、700、750和800 ^oC下的功率密度分别为1.68、4.70、12.40、28.08和54.78 mW·cm ^-2,表现出一定的电化学性能和较好的稳定性.     

Temperature programmed reduction of silica supported nickel catalysts

Nickel oxide promoted catalysts are prepared by simple precipitation, precipitation from homogeneous solution and impregnation methods and their reduction behavior is monitored with temperature programmed reduction (TPR) technique. The effect of different parameters such as metal loading, method of preparation and heat treatment temperature are also observed on the reducibility of the catalysts. It is observed that reduction temperature increases with the increase of calcination temperature. Results indicate that the interactions between nickel oxide and silica begin with the increase of calcination temperature which leads to the formation of nickel hydrosilicates and are responsible for high temperature reduction peaks.