Hierarchy and Social Pressure - Before discussing methods to address this hypothesis, it’s important to note that these social interactions may be normal. Aggression is a normal part of baboon behavior; therefore attempts to eliminate it are not appropriate. However, it should occur at levels that are not considered excessive. Environmental improvements that can meet the needs of the baboon, ‘Sam’ by providing him solace and escape will help to address the social situation. Exhibit features that create multiple levels, numerous pathways, and visual partitioning of the exhibit should be added. Resources such as enrichment, feeding stations, etc. should be broadly distributed in quantities that allow all animals to get their fair share. Cooperative feeding, a technique to enhance pro-social behavior, could be initiated between group members and ‘Sam’, or between other
Thinking is the actualization of the way the brain works. According to Torrance creativity is “being sensitive to problems, insufficiencies, shortage of information, nonexistent elements, and noncompatibility; identifying challenges, seeking for solutions, estimation and hypothesizing or modifying hypothesises in relation with insufficiencies, selecting and trying one of the solutions, retrial, and drawing conclusions accordingly . There are three basic ideas about thinking, namely: (1) Thinking is cognitively that happens "internally" in thinking but decisions are made through behavior, (2) Thinking is a process that involves some knowledge manipulation in the cognitive system, (3) thinking it is direct in nature and generating behavior that solves the problem or goes straight to the solution .Creative students differ from students who are less creative. Creative students are more inclined to come up with questions that can help them find answers when solving a problem. A student will be easy to have the ability to think creatively in mathematics if when he received a lesson, the way given to him can cultivate thinking and creativity through a strategy used by teachers. As mentioned earlier, creative thinking is a mind-set based on a method that encourages people to produce creative products. This means that creative thinking students will always try to find solutions to problems that are different from the usual and varied. So, what is meant by creative thinking in mathematics is the ability of a person to be able to solve a problem of mathematics by finding solutions that vary and diverse while looking at the quality of solutions. Thus the learning of mathematics is not perceived as monotonous and boring.
In this paper we propose EO-QAP: an Extremal Optimization (EO) proce- dure for QAP. EO is a nature-inspired general-purpose meta-heuristics to solve combinatorial optimization problems . This local search procedure, a priori, has several advantages: it is easy to implement, it does not get confounded by local minima and takes only one adjustable parameter. We experimentally demonstrate that EO-QAP performs well on the set of QAPLIB benchmark in- stances. It is, however, known that it is difficult with EO to have fine control on the trade-off between search intensification and diversification: some strate- gies have been proposed to overcome this limitation , but they entail a more complex tuning process. In this paper we put forth two other approaches which contribute to a more effective handling of QAP using EO: firstly, we propose a simple extension to the original EO which allows the user to have more control over the stochastic behavior of the algorithm. Secondly, we propose to use coop- erative parallelism to promote more intensification and/or diversification. Our implementation uses a parallel framework  written in X10 [8,9]. We show that the cooperative parallel version behaves very well on the hardest instances.
solving informed the researcher’s identification of particular behaviors of cooperativeproblemsolving during play. With this framework, the researcher micro-analyzed seven children’s footage and developed her own coding system. The rationale for using her framework includes (a) same age range of child participants (four- and five-year-olds) and (b) similar research focus that studied children’s cooperativeproblemsolving in a playful, child-directed activity in an experimental setting. In her study, Ramani distinguished children’s cooperativeproblemsolving into two macro-level behaviors – cooperative behaviors and communication that included a certain number of micro-level behaviors (see Table 3.5). Further, Ramani (2012) created five composites with an integration of the micro-level behaviors, that included (a) cooperative interaction (asking questions, explanations, attention directing, and physical demonstration), (b) joint communication (suggestions, narration, and agreements), (c) shared task responsibility (coordinated action, negotiation, and dividing labor), (d) observational learning (observation and imitation), and (e) unproductive behavior and communication (controlling, disagreements, and verbalization to experimenter).
The current study is the first comparative social cogni- tive study conducted with otters. It is therefore a first step to explore the socio-cognitive capacities of these species known for traits suggested to be an indication of complex cognitive skills in other taxa, e.g., cooperative breeding and hunting, large relative neocortex size (compared to other carnivores; Dunbar and Bever 1998), neophilia and social complexity (Byrne and Whiten 1988; Humphrey 1976). We have a clear-cut result: Both giant otters and Asian small-clawed otters succeeded in solving the social prob- lem of our version of the loose string task when pairs could reach the ends of the rope simultaneously. In both species, this success broke down as soon as a delay was introduced. Otters’ failure to wait for a partner suggests that they either did not understand the task contingencies or could not inhibit pulling a rope as soon as it was available. This initial finding should be explored in more detail in the future.
Evidence has supported the effectiveness of the Osborn-Parnes CPS model on enhancing one’s creative thinking ability (e.g., Basaduar, Graen & Green, 1982 Fontenot, 1992; Titus, 2000; Wang & Horng, 2002). More recently, it begins to have some supports for its effects on actual work outputs and problem-solving processes. For example, Fontenot (1992) employed CPS train- ing to 62 american business managers. Thirty-four participants’ post-CPS problemsolving per- formance in solving a business case was compared to the pre-CPS problemsolving performance of the other 28 managers. The results showed that eight hours of the CPS training significantly in- creased a participant’s problem-solving performance measured by the fluency in data finding, and the fluency and flexibility in problem finding. Wang Horng 2002 made an attempt to inves- tigate the effects of CPS training on R D performances with 106 R D workers of a large manu- factory company in Taiwan. Seventy-one of them volunteered to participate in the CPS training and were divided into three groups. Each group received 12 hours for CPS training and two fol- low-up training sessions over a one-year period in a time-series design. The results showed that participant’s scores on fluency and flexibility of ideas were higher after the CPS training. In terms of R D performance, participants’ number of co-authored service projects increased significantly from pretest to posttest, whereas no such change was observed among the remaining 35 R D workers who did not participate in the CPS. The purpose of this study is to examine whether or not the effect of CPS can be extended to cognitive processes in managerial problemsolving.
A. Describe the desired behavior of the program: Our program should display a prompt for the Celsius temperature on the screen, read that temperature from the keyboard, compute the corresponding Fahrenheit temperature, and display that temperature along with a descriptive label on the screen.
function Tree-Search ( problem, strategy ) returns a solution, or failure
initialize the search tree using the initial state of problem
if there are no candidates for expansion then return failure choose a leaf node for expansion according to strategy
Some linguists may feel that some positions, some syntactic relations, are natural, and that they tacitly interpret some elements in those positions. From the casual observation that, in some languages, some semantic relations between items are expressed by having these items occupy certain positions in a sentence, it is an easy step to assume that the mapping of semantic representations onto morphosyntax should be universally positional, i.e. that this is the ‘conceptually natural’ syntactic relation. But this idea faces the problem of accounting for all the cases in which languages use other means than linear juxtaposition to express relations, such as intonation, case marking, the use of loci in sign languages, etc. These conflicting facts force the adoption of a more complex model of grammar. For instance, case-marked elements typically have a relatively free ordering. This forces the adoption of costly constructs, such as assuming that Case-marked elements have a scrambling feature that induces pied-piping even after Case assignment, with the pied-piped element ‘attracted’ by a higher probe (Chomsky 2000). So Case-marking languages mysteriously happen to have extra mechan- isms that conspire to give the impression of a freer order. 20 The positional view
When a group of agents are engaged in a cooperative activity they must have a joint com- mitment to the overall aim, as well as their individual commitments to the specific tasks that they have been assigned. This joint commitment shares the persistence property of the individual commitment; however, it differs in that its state is distributed among the team members. To minimise the potential drawbacks of this distribution, an appropriate social convention must be put in place. This social convention identifies the conditions under which the joint commitment can be dropped, and also describes how the agent should behave to- wards its fellow team members. For example, if an agent drops its joint commitment because it believes that the goal will never be attained, then it is part of the notion of ‘cooperativeness’ which is inherent in joint action that it informs all of its fellow team members of its change of state. In this context, social conventions provide general guidelines, and a common frame of reference in which agents can work. By adopting a convention, every agent knows what is expected both of it, and of every other agent, as part of the collective working towards the goal, and knows that every other agent has a similar set of expectations.
The multileveled nature of analogy can perhaps be understood in the context of Kintsch and Van Dijk's (1978) theory of prose representation. They argue that the understanding process may involve the iterative application of a set of inference rules that generate increasingly abstract "macrostructure" representations of a prose passage. These macrostruc- tures essentially correspond to summaries of the passage at various levels of generality. In the case of a problem-oriented story such as the Attack- Dispersion story, an abstract level of macrostructure might state a general solution principle (e.g., to destroy a target when direct application of a large force is harmful to the surrounding area, disperse the attacking forces, and have them converge at the target). The process of extracting a solution principle might thus be viewed as a special case of the process of deriving macrostructures for a body of information. While much remains to be learned about how this process operates, the three specific inference rules proposed by Kintsch and Van Dijk (which they term "deletion," "generalization," and "construction") would seem readily applicable to the type of story analogies we are considering here.
Consider again the example of finding a life partner in the context of this model. First, the problem is recognized through attention to cues in the environment, such as noting who you are, what your needs are, and what type of person might possess the qualities you are seeking. Second, as you think of where to find such an individual, you consider analogous problems, such as how you went about selecting a suitable career or how you found friends when you moved to a new area. Third, you screen these possible analogous representations for importance. Whereas the strategy of choosing friends may have been governed by proximity and common interests, you may find that the strategy of choosing a career is more ap- propriate to finding a life partner if your career is one that you are pas- sionate about, that takes into account your values and interests, and that is something that you are committed to for the long term (as opposed to a superficial friendship, which may last only as long as you remain in the same city or neighborhood). Fourth, you examine the goals and constraints of the problems. For example, it may be more important to consider the compatibility of lifestyles (e.g., career, values) with a life partner than it is with a friend. That is, you may maintain friendships with individuals whose political or religious ideals are very different from your own, but you would be less likely to choose to initiate a romantic relationship with someone with incompatible values. Fifth and finally, all of the consider- ations you have identified as relevant to finding a life partner are repre- sented in a way that makes it possible to conceptualize who that person might be.
This problem is relatively simple, compared to other information which may be useful to a good player. (The problem is somewhat harder, even if three or more diamonds are needed in one hand, or if a particular card, for example a Queen, is needed with the four or five diamonds.) But we can find good approximations to the probabilities in this kind of problem, using a computer.
• Diverge before you converge: Start by creating many different ideas, one option is to ask the participants to write down as many ideas as they can individually for five to 10 minutes. This gives introverts a chance to maximize their contribution, and having lots of ideas on paper before the discussion begins prevents the group from rallying around any specific solution too soon. Use a common framework for each idea that includes the resources or processes needed to make it a reality; and how the solution will ‘solve the problem’. This allows an ‘apples-to-apples’ comparison of the ideas.
To this end, we need to acknowledge that problemsolving is not a consistent field of research even though the definitions of problemsolving in PISA have a lot in com- mon. This situation is clearly reflected by the different assessment instruments found in the PISA cycles over the last decade. However, besides the differences mentioned, there is considerable overlap with regard to the cognitive processes that have been targeted (e.g., the notion of knowledge acquisition and knowledge application is found in all conceptualizations of PISA) and with regard to the intention to move beyond the mere assessment of domain-specific abilities in the context of an educational large-scale as- sessment. To further deepen our understanding of problemsolving—be it embedded into a specific content domain (OECD, 2003), as an individual transversal skill (OECD, 2012), or in collaboration with others (OECD, 2015)—further research needs to address the theoretical understanding and the empirical side of problemsolving. In order to make some suggestions for this facilitation, we will next describe how bringing together edu- cational assessment and cognitive science, in which problem-solving research is rooted, may benefit both sides and the field of problemsolving in general. Originally, research on problemsolving emerged in experimental cognitive psychology (cf. Jonassen, 2007), and a strong link between educational assessment and cognitive psychology has yet to be established despite the potentials inherent in such integration. We see several ways in which the cooperation between the disciplines of cognitive psychology and educational assessment can be further extended in the future. For instance, open questions in assess- ment could be addressed by experimental laboratory studies, whereas log data provided by computer-based assessment in LSAs may prove valuable for understanding cognitive processes and behavioral patterns.
Risk-taking behavior further increases at the end of adolescence and with transition to adulthood. Risky behaviors among adolescents include crime, violence, smoking, alcohol or substance use, risky (drunk driving, without seat belt, fast, without driving license) driving, theft, early sexual activity, self-harm behavior, skipping school or eloping, dropping out, indifference to courses, lack of professional or social skills, unhealthy eating behaviors and sedentary life (Aras et al., 2007) . The reasons of risk-taking behavior in adolescents are to join peer groups, to oppose family repression and traditional social structure, to control one’s own life, to wait for approval of identity, and failure to cope with feelings such as failure, anxiety, and inability (Karahan et al., 2006) . It is reported that resistance to restraint, coping skills, personality traits such as self-control and insight, ties with family, school and other social groups keep adolescents away from risky behaviors. Adolescents may be more prone to their peers who have risky behaviors because they think that they will not reach their desires by working or obeying rules, they cannot find closeness in their family, and they have weak ties in their family (Yılmaz, 2000) .
By representing the problem decomposition structure explicitly, and capturing within it these kinds of task relationships, we can employ a variety of coordination mechanisms. For example, an agent that provides an enabling result to another can use the task structure representation to detect this relationship, and can then bias its proc- essing to provide this result earlier. In fact, it can use models of task quality versus time curves to make commitments to the recipient as to when it will generate a result with sufficiently high quality. In situations where there are complex networks of non- local task interrelationships, decisions of this kind of course get more difficult. Ulti- mately, relatively static organizational structures, relationships, and communication strategies can only go so far. Going farther means that the problem-solving agents need to analyze their current situation and construct plans for how they should interact to solve their problems.
Specialist mathematics, statistics and operational research (MSOR) programmes are recognised as intellectually demanding, and require students to formulate, abstract, and solve mathematical problems in a rigorous way. The process of developing the skills to do this well and communicate results can be challenging for learners as it requires a deep understanding of themes in mathematics as well as methods for solving problems. In this article we demonstrate how elements of Freudenthal’s Realistic Mathematics Education can be applied to teaching problemsolving in undergraduate mathematics programmes. We describe an approach that moves away from standard practices and goes beyond problemsolving methods to develop an understanding of common themes in mathematics.