The ESG Value Opportunity: A Decision Point for Utilities

2021 ◽  
Vol 38 (5) ◽  
pp. 10-17
Author(s):  
Saba Khalid ◽  
Kaylene Hung ◽  
Jeremy Wiley
Keyword(s):  
Decision Point

Perspectives on Ethics of AI

2020 ◽  
pp. 419-441
Author(s):  
Benjamin Kuipers
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Feedback Mechanism ◽  
Computational Approach ◽  
Human Society ◽  
Decision Point ◽  
Free Riders ◽  
Ethical Knowledge ◽  
Distributed Intelligent Systems ◽  
Lesser Evil

This chapter describes a computational view of the function of ethics in human society and discusses its application to three diverse examples. First, autonomous vehicles are individually embodied intelligent systems that act as members of society. The ethical knowledge needed by such an agent is not how to choose the lesser evil when confronted by a Deadly Dilemma, but how to recognize the upstream decision point that makes it possible to avoid the Deadly Dilemma entirely. Second, disembodied distributed intelligent systems like Google and Facebook provide valuable services while collecting, aggregating, and correlating vast amounts of information about individual users. With inadequate controls, these corporate systems can invade privacy and do substantial damage through either correct or incorrect inferences. Third, acceptance of the legitimacy of the society by its individual members depends on a general perception of fairness. Rage about unfairness can be directed at individual free-riders or at systematic inequality across the society. Ultimately, the promise of a computational approach to ethical knowledge is not simply ethics for computational devices such as robots. It also promises to help people understand the pragmatic value of ethics as a feedback mechanism that helps intelligent creatures, human and nonhuman, live together in thriving societies.

Falsification of Hybrid Systems Using Adaptive Probabilistic Search

2021 ◽  
Vol 31 (3) ◽  
pp. 1-22
Author(s):  
Gidon Ernst ◽  
Sean Sedwards ◽  
Zhenya Zhang ◽  
Ichiro Hasuo
Keyword(s):  
Hybrid Systems ◽  
Input Signal ◽  
Tree Search ◽  
Single Trial ◽  
Decision Point ◽  
Directed Tree ◽  
Probabilistic Choice ◽  
Original Algorithm ◽  
Fine Grained ◽  
Probabilistic Search

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.

Design Option Decision Tree: A Method for Schematic Analysis of a Design Problem and Integration of Human Factors Data

1974 ◽  
Vol 18 (3) ◽  
pp. 368-375
Author(s):  
William B. Askren ◽  
Kenneth D. Korkan
Keyword(s):  
Decision Tree ◽  
Human Factors ◽  
Design Problem ◽  
Aircraft Design ◽  
Decision Point ◽  
Specific Design ◽  
Design Problems ◽  
Design Option ◽  
Design Options ◽  
Evaluation Parameters

A Design Option Decision Tree (DODT) is a graphic means of showing the design options available at each decision point in the design process. Several examples of DODTs for aircraft design problems are shown. The procedures for developing a DODT are described. A proposed method for use of the DODT to resolve a design problem is presented. This method includes evaluating the design options in the Tree for impact on the system, and tracing paths through the Tree as dictated by specific design goals. The use of human factors data as one of the evaluation parameters is illustrated. The paper concludes with a discussion of other uses of a DODT.

Positioning the Tata Nano (B)

2017 ◽  
pp. 1-2
Author(s):  
Alice M. Tybout ◽  
Natalie Fahey
Keyword(s):  
Production Capacity ◽  
Frame Of Reference ◽  
Product Launch ◽  
Limited Capacity ◽  
Brand Positioning ◽  
Decision Point ◽  
Safety Issues ◽  
Indian Consumers ◽  
Two Stages ◽  
Made In

The case focuses on positioning a new brand, the Tata Nano. The car has been widely publicized as the world's cheapest car at Rs.1 lakh. Students must consider the gap between the ultimate target, the huge emerging middle class of Indian consumers, and the limited capacity and distribution available in choosing a target. They also must select between alternative competitive frames and the various points of difference they highlight. The case unfolds in two stages. The first decision point is in 2009, at the launch of the time of the product launch. The second decision point is 18 months later, after production capacity has increased and some product safety issues have arisen.The primary goal of the case is to illustrate the choices made in developing a strong brand positioning and the interrelationship between these choices. Students select a target and an appropriate competitive frame of reference and point of difference for that target and summarize these elements in a positioning statement. The case also highlights importance of making promotion and distribution decisions that are consistent with the positioning.

Software-Defined Storage

2018 ◽  
pp. 268-290
Author(s):  
Himanshu Sahu ◽  
Ninni Singh
Keyword(s):  
Decision Making ◽  
It Infrastructure ◽  
Decision Point ◽  
Storage Devices ◽  
Commodity Hardware ◽  
Dynamic Solution ◽  
Storage Solutions ◽  
Data Plane ◽  
User Need ◽  
Three Components

SDS along with SDN and software-defined compute (SDC; where in computing is virtualized and software defined) creates software-defined infrastructure (SDI). SDI is the set of three components—SDN, SDS, and SDC—making a new kind of software-defined IT infrastructure where centralization and virtualization are the main focus. SDI is proposed to have infrastructure developed over commodity hardware and software stack defined over it. SDS is exploiting the same concept of decoupling and centralization in reference to storage solutions as in SDN. The SDN works on decoupling the control plane with the data plane from a layer, three switches, or router, and makes a centralized decision point called the controller. The SDS works in a similar way by moving the decision making from the storage hardware to a centralized server. It helps in developing new and existing storage solutions over the commodity storage devices. The centralization helps to create a better dynamic solution for satisfying the customized user need. The solutions are expected to be cheaper due to the use of commodity hardware.

A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

2019 ◽  
Vol 233 (6) ◽  
pp. 1014-1028
Author(s):  
Lin Wang ◽  
Zhiqiang Lu ◽  
Yifei Ren
Keyword(s):  
Service Level ◽  
Condition Based Maintenance ◽  
Uncertain Demand ◽  
Programming Approach ◽  
Production Plan ◽  
Rolling Horizon ◽  
Decision Point ◽  
Total Cost ◽  
New Information ◽  
Maintenance Plan

In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.

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