Adapting to the task environment: Explorations in expected value

2005 ◽  
Vol 6 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Wayne D. Gray ◽  
Michael J. Schoelles ◽  
Chris R. Sims
Keyword(s):  
Expected Value ◽  
Task Environment

Viking Development of an Infantry Scenario Multi-Task Environment

2002 ◽  
Author(s):  
Wayne C. Harris ◽  
Raja Parasuraman ◽  
Marla Zinni ◽  
Peter A. Hancock ◽  
Scot C. Harris
Keyword(s):  
Task Environment

The Expected Value Premium

CFA Digest ◽  
2008 ◽  
Vol 38 (3) ◽  
pp. 35-36
Author(s):  
Michael Kobal
Keyword(s):  
Expected Value ◽  
Value Premium

First-Year Engineering Computing Courses in Canadian Engineering Programs

1969 ◽  
Author(s):  
Sean Maw ◽  
Janice Miller Young ◽  
Alexis Morris
Keyword(s):  
Problem Solving ◽  
Engineering Students ◽  
Expected Value ◽  
Knowledge Development ◽  
First Year ◽  
Recent Past ◽  
Engineering Programs ◽  
Digital Computation ◽  
Pedagogical Philosophies ◽  
Introductory Computing

Most Canadian engineering students take a computing course in their first year that introduces them to digital computation. The Canadian Engineering Accreditation Board does not specify the language(s) that can or should be used for instruction. As a result, a variety of languages are used across Canada. This study examines which languages are used in degree-granting institutions, currently and in the recent past. It also examines why institutions have chosen the languages that they currently use. In addition to the language used in instruction, the types and hours of instruction are also analyzed. Methods of instruction and evaluation are compared, as well as the pedagogical philosophies of the different programs with respect to introductory computing. Finally, a comparison of the expected value of this course to graduates is also presented. We found a more diverse landscape for introductory computing courses than anticipated, in most respects. The guiding ethos at most institutions is skill and knowledge development, especially around problem solving in an engineering context. The methods to achieve this are quite varied, and so are the languages employed in such courses. Most programs currently use C/C++, Matlab, VB and/or Python.

ANALISIS RISIKO OPERASIONAL MENGGUNAKAN PENDEKATAN DISTRIBUSI KERUGIAN DENGAN METODE AGREGAT

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Financial Institution ◽  
Expected Value ◽  
Insurance Companies ◽  
Limited Data ◽  
Loss Distribution Approach ◽  
Potential Loss ◽  
The Impact

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.

Information is Relational, Affordances Are Not

2018 ◽  
Author(s):  
Andrew D Wilson
Keyword(s):  
Perception And Action ◽  
Task Environment ◽  
Perceptual Information ◽  
Paper Briefly ◽  
Recent Approach ◽  
Dispositional Properties

Ever since Gibson proposed the concept of ‘affordances’ in 1979, we have been arguing about the best way to formalize the idea in a way that can allow us to successfully explain behavior. The first approach was to consider them as dispositional properties of task environment which can support skillful perception and action. A more recent approach considers them more broadly as relations between properties of organisms and their environments. This expands the spatial and temporal scope of affordances; we stand in many kinds of relations to our physical but also social and cultural environments. Relational affordances are therefore offered as an ecological way to explain behaviors in these domains. However, these relational affordances do not, as a rule, interact with perceptual media and therefore do not create perceptual information about themselves. This means they cannot be perceived, which in turn means they cannot play any role in an ecological explanation of a behavior. This paper briefly reviews the dispositional vs relational accounts of affordances, explains the problem, and proposes an information-based alternative (building on Golonka, 2015). Affordances are not relational, but fortunately information is, and this is where we should focus our attention.

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