The emergence of choice: Decision-making and strategic thinking through analogies

Consider the chess game: When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve

Decision-making and strategic thinking through analogies

When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? Consider the chess game: how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve

Role-play and the discussion of the controversial issues during teacher training: teaching practices in favor of a Sustainable Development?

In a society that is constantly changing and deeply marked by advances in science and technology, it is essential to invest in a training that values personal and social development, as well as the understanding of the interaction Science-Technology-Society-Environment (CTSA). A science education focused on knowledge and understanding of science and technology and its role in our daily lives allows individuals to engage and understand these discussions on these issues, as well as their social implications. Therefore, it is crucial to provide students with teaching-learning situations that can enable them for a more reflected and critical participation in society, that are likely to trigger an increase in cognitive, communicative, social and democratic processes, paving the way for scientific literacy and a participatory and democratic citizenship. In this social context, the study of controversial issues of social-scientific and social-environmental nature, it is constituted as a rich learning context as they are associated with existing differences between the different actors involved in the discussion, regarding the assessment of the validity and credibility of the scientific issues that are involved. It is the case, for example, of differences related to some practices developed by man that can endanger our health or the environment. For Sadler, Barab and Scott (2007), a social-scientific reasoning should involve: the recognition of the complexity of the issues under study, the analysis of the issues in their multiple perspectives, bear in mind the notion that the issues are under investigation and doubt the potentially tendentious information. In this context, these issues cannot be simplified but seen in all their complexity (in all dimensions involved). Based on these assumptions, it was developed an investigation-action with future teachers and educators in order to access the potentialities and limitations of the discussion of socio-scientific and socio-environmental issues using the role-playing in environmental education classes. The activity was focused on a current environmental problem in our society – the construction of dams – raising the awareness of students about the risks and the interests that are involved and in which participate different groups of society, like scientists, environmentalists, politicians and economists. If the seriousness of the environmental problems and the fast rhythm that current societies live ask for an intervention and awareness in the environmental area, in a perspective of continuity of life and sustainable balance, it is essential to reach the population from which young people are part of and, consequently, it is the school’s responsibility to make a statement in this sense. The obtained data through the activity assessment questionnaire, the interview, field notes and the final reflection carried out by work groups helped confirm that this didactic experience enabled the development of many skills related to: a) the substantive knowledge; b) didactic knowledge; c) reasoning; d) communication; and d) attitudes. Some of the negative aspects of the performed dynamic were related to how the teacher managed the discussion but it was also pointed out the less positive aspects the communicative skills and the team work skills evidenced by students. However, as it was observed in other studies about the same issue, not all groups were able to take a decision regarding the feasibility and reasonableness of more investments for the construction of dams in our country, based on the example of the Alqueva dam. In this type of activity it is difficult to reach a consensus among all group members. There are many aspects to consider and the more awareness students take in relation to the factors involved in the problem and its implications, the more confused they become. The most important aspect is to inform the students of the various dimensions involved and enable them to access accurate and reliable information.info:eu-repo/semantics/publishedVersio

Approximation Algorithms for Distributionally Robust Stochastic Optimization with Black-Box Distributions

Two-stage stochastic optimization is a framework for modeling uncertainty, where we have a probability distribution over possible realizations of the data, called scenarios, and decisions are taken in two stages: we make first-stage decisions knowing only the underlying distribution and before a scenario is realized, and may take additional second-stage recourse actions after a scenario is realized. The goal is typically to minimize the total expected cost. A criticism of this model is that the underlying probability distribution is itself often imprecise! To address this, a versatile approach that has been proposed is the {\em distributionally robust 2-stage model}: given a collection of probability distributions, our goal now is to minimize the maximum expected total cost with respect to a distribution in this collection. We provide a framework for designing approximation algorithms in such settings when the collection is a ball around a central distribution and the central distribution is accessed {\em only via a sampling black box}. We first show that one can utilize the {\em sample average approximation} (SAA) method to reduce the problem to the case where the central distribution has {\em polynomial-size} support. We then show how to approximately solve a fractional relaxation of the SAA (i.e., polynomial-scenario central-distribution) problem. By complementing this via LP-rounding algorithms that provide {\em local} (i.e., per-scenario) approximation guarantees, we obtain the {\em first} approximation algorithms for the distributionally robust versions of a variety of discrete-optimization problems including set cover, vertex cover, edge cover, facility location, and Steiner tree, with guarantees that are, except for set cover, within $O(1)$-factors of the guarantees known for the deterministic version of the problem