Decision making at the front end of innovation: The hidden influence of knowledge and decision criteria

Decision making at the front end of innovation is critical for the success of companies. This paper presents a method, called decision making based on knowledge (DeBK), which was created to analyze the decision-making process at the front end. The method evaluates the knowledge of project information and the importance of decision criteria, compiling a measure that indicates whether decisions are founded on available knowledge and what criteria are in fact being considered to delineate them. The potential contribution of DeBK is corroborated through two projects that faced decision-making issues at the front end of innovation. © 2014 RADMA and John Wiley & Sons Ltd.

Motor effort alters changes of mind in sensorimotor decision making.

After committing to an action, a decision-maker can change their mind to revise the action. Such changes of mind can even occur when the stream of information that led to the action is curtailed at movement onset. This is explained by the time delays in sensory processing and motor planning which lead to a component at the end of the sensory stream that can only be processed after initiation. Such post-initiation processing can explain the pattern of changes of mind by asserting an accumulation of additional evidence to a criterion level, termed change-of-mind bound. Here we test the hypothesis that physical effort associated with the movement required to change one's mind affects the level of the change-of-mind bound and the time for post-initiation deliberation. We varied the effort required to change from one choice target to another in a reaching movement by varying the geometry of the choice targets or by applying a force field between the targets. We show that there is a reduction in the frequency of change of mind when the separation of the choice targets would require a larger excursion of the hand from the initial to the opposite choice. The reduction is best explained by an increase in the evidence required for changes of mind and a reduced time period of integration after the initial decision. Thus the criteria to revise an initial choice is sensitive to energetic costs.

Delayed-decision making in noisy bistable switches

Bistable switches are frequently encountered in biological systems. Typically, a bistable switch models a binary decision where each decision corresponds to one of the two stable equilibria. Recently, we showed that the global decision-making process in bistable switches strongly depends on a particular equilibrium point of these systems, their saddle point. In particular, we showed that a saddle point with a time-scale separation between its attractive and repulsive directions can delay the decision-making process. In this paper, we study the effects of white Gaussian noise on this mechanism of delayed decision-making induced by the saddle point. Results show that the mean decision-time strongly depends on the balance between the initial distance to the separatrix and the noise strength. © IFAC.

Compact Reconfigurable Binary-Decision-Diagram Logic Circuit on a GaAs Nanowire Network

We describe a reconfigurable binary-decision-diagram logic circuit based on Shannon's expansion of Boolean logic function and its graphical representation on a semiconductor nanowire network. The circuit is reconfigured by using programmable switches that electrically connect and disconnect a small number of branches. This circuit has a compact structure with a small number of devices compared with the conventional look-up table architecture. A variable Boolean logic circuit was fabricated on an etched GaAs nanowire network having hexagonal topology with Schottky wrap gates and SiN-based programmable switches, and its correct logic operation together with dynamic reconfiguration was demonstrated.

An automata theoretic decision procedure for the propositional mu-calculus

The propositional mu-calculus is a propositional logic of programs which incorporates a least fixpoint operator and subsumes the propositional dynamic logic of Fischer and Ladner, the infinite looping construct of Streett, and the game logic of Parikh. We give an elementary time decision procedure, using a reduction to the emptiness problem for automata on infinite trees. A small model theorem is obtained as a corollary.

黄土高原降雨径流调控利用潜力定量评价模型

降雨径流的调控利用是缓解黄土高原干旱缺水与控制水土流失的有效手段,研究区域降雨径流调控利用潜力的定量评价对黄土高原降雨径流合理利用的宏观决策与规划设计具有重要意义。以黄土高原为例,将可以调控利用的最大降雨径流量作为资源化潜力值,从宏观尺度上,系统分析了影响该潜力的各个因素,确定出黄土高原降雨径流调控利用潜力的各项评价指标,利用GIS技术,建立了降雨径流各个影响因素的专题图层,提取出各个影响因素专题信息。在上述基础上,引入人工神经网络建模方法,建立了黄土高原降雨径流调控利用潜力BP网络模型,并利用实际资料对网络模型进行了训练和预测,取得了较好的结果。评价模型可供黄土高原降雨径流调控利用及其生态与环境保护工作参考。

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

基于GIS的区域水土流失评价模型

区域水土流失评价对于水土流失区域治理的宏观决策、规划等具有十分重要的意义 .本文以黄土高原地区为例 ,结合影响水土流失的各个因子 ,分析了该区的自然特征 ,确定了进行区域水土流失评价的各项指标 (模型参数 ) ,并运用 GIS,集成了多种来源、多种比尺和多种类型的数据 ,建立了该区的水土流失宏观定量评价模型

基于RS和GIS的区域水土流失快速定量评价方法

区域水土流失的快速调查和评价对于水土保持宏观决策、灾情快速反应以及水土流失定期公告制的建立具有十分重要的意义。立足于区域水土流失问题的探索和 3S技术应用的研究 ,提出了区域水土流失宏观快速定量评价的方法及其实现的技术路线 ,并以整个黄土高原为研究区进行了实例研究 ,确定了一套适于该区水土流失宏观定量评价的指标 ,建立了相应的评价模型 ,并利用该模型对研究区一定年限内的水土流失状况进行了评价 ,获得了令人满意的结果

Amplified electrochemical aptasensor taking AuNPs based sandwich sensing platform as a model

Here, we report a sensitive amplified electrochemical impedimetric aptasensor for thrombin, a kind of serine protease that plays important role in thrombosis and haemostasis. For improving detection sensitivity, a sandwich sensing platform is fabricated, in which the thiolated aptamers are firstly immobilized on a gold substrate to capture the thrombin molecules, and then the aptamer functionalized Au nanoparticles (AuNPs) are used to amplify the impedimetric signals.