A pedagogic appraisal of the Priority Heuristic

We have explored how science and mathematics teachers made decisions when confronted with a dilemma in which a fictitious young woman, Deborah, may choose to have an operation that might address a painful spinal condition. We sought to explore the extent to which psychological heuristic models, in particular the Priority Heuristic, might successfully describe the decision-making process of these teachers and how an analysis of the role of personal and emotional factors in shaping the decision-making process might inform pedagogical design. A novel aspect of this study is that the setting in which the decision-making process is examined contrasts sharply with those used in psychological experiments. We found that to some extent, even in this contrasting setting, the Priority Heuristic could describe these teachers' decision-making. Further analysis of the transcripts yielded some insights into limitations on scope as well the richness and complexity in how personal factors were brought to bear. We see these limitations as design opportunities for educational intervention

The Influence of Experimental and Computational Economics: Economics Back to the Future of Social Sciences

Economics has been a most puzzling science, namely since the neoclassical revolution defined the legitimate procedures for theorisation and quantification. Its epistemology has based on farce: decisive tests are not applied on dare predictions. As a consequence, estimation has finally been replaced by simulation, and empirical tests have been substituted by non-disciplined exercises of comparison of models with reality. Furthermore, the core concepts of economics defy the normally accepted semantics and tend to establish meanings of their own. One of the obvious instances is the notion of rationality, which has been generally equated with the apt use of formal logic or the ability to apply econometric estimation as a rule of thumb for daily life. In that sense, rationality is defined devoid of content, as alien to the construction of significance and reference by reason and social communication. The contradictory use of simulacra and automata, by John von Neumann and Herbert Simon, was a response to this escape of economic models from reality, suggesting that markets could be conceived of as complex institutions. But most mainstream economists did not understand or did not accept these novelties, and the empirical inquiry or the realistic representation of the action of agents and of their social interaction remained a minor domain of economics, and was essentially ignored by canonical theorizing. The argument of the current paper is based on a survey and discussion of the twin contributions of experimental and computational economics to these issues. Although mainly arising out of the mainstream, these emergent fields of economics generate challenging heuristics as well as new empirical results that defy orthodoxy. Their contributions both to the definition of the social meanings of rationality and to the definition of a new brand of inductive economics are discussed.

Why and How Identity Should Influence Utility

This paper provides an argument for the advantage of a preference for identity-consistent behaviour from an evolutionary point of view. Within a stylised model of social interaction, we show that the development of cooperative social norms is greatly facilitated if the agents of the society possess a preference for identity consistent behaviour. As cooperative norms have a positive impact on aggregate outcomes, we conclude that such preferences are evolutionarily advantageous. Furthermore, we discuss how such a preference can be integrated in the modelling of utility in order to account for the distinctive cooperative trait in human behaviour and show how this squares with the evidence

Why and How Identity Should Influence Utility

This paper provides an argument for the advantage of a preference for identity-consistent behaviour from an evolutionary point of view. Within a stylised model of social interaction, we show that the development of cooperative social norms is greatly facilitated if the agents of the society possess a preference for identity consistent behaviour. As cooperative norms have a positive impact on aggregate outcomes, we conclude that such preferences are evolutionarily advantageous. Furthermore, we discuss how such a preference can be integrated in the modelling of utility in order to account for the distinctive cooperative trait in human behaviour and show how this squares with the evidence.cognitive dissonance; fairness; identity; reciprocity; social Norms; social preferences; utility

Digital or Diligent? Web 2.0's challenge to formal schooling

This paper explores the tensions that arise for young people as both 'digital kids' and 'diligent students'. It does so by drawing on a study conducted in an elite private school, where the tensions between 'going digital' and 'being diligent' are exacerbated by the high value the school places on academic achievement, and on learning through digital innovation. At the school under study, high levels of intellectual and technological resourcing bring with them an equally high level of expectation to excel in traditional academic tasks and high-stakes assessment. The students, under constant pressure to perform well in standardised tests, need to make decisions about the extent to which they take up school-sanctioned digitally enhanced learning opportunities that do not explicitly address academic performance. The paper examines this conundrum by investigating student preparedness to engage with a new learning innovation – a student-led media centre – in the context of the traditional pedagogical culture that is relatively untouched by such digital innovation. The paper presents an analysis of findings from a survey of 481 students in the school. The survey results were subjected to quantitative regression tree modelling to flesh out how different student learning dispositions, social and technological factors influence the extent to which students engage with a specific digital learning opportunity in the form of the Web 2.0 Student Media Centre (SMC) designed to engage the senior school community in flexible digital-networked learning. What emerges from the study is that peer support, perceived ease of use and usefulness, learning goals and cognitive playfulness are significant predictors of the choices that students make to negotiate the fundamental tensions of being digital and/or diligent. In scrutinising the tensions around a digital or a diligent student identity in this way, the paper contributes new empirical evidence to understanding the problematic relationship between student-led learning using new digital media tools and formal schooling

Sequential Selection of Correlated Ads by POMDPs

Online advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions but also lower the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and ads is a fine start but cannot solve the problem completely because good matching does not necessarily lead to good payoff. Moreover, with the limited display impressions, we need to balance the need of selecting ads to learn true ad payoffs (exploration) with that of allocating ads to generate high immediate payoffs based on the current belief (exploitation). In this paper, we address the problem by employing Partially observable Markov decision processes (POMDPs) and discuss how to utilize the correlation of ads to improve the efficiency of the exploration and increase ad incomes in a long run. Our mathematical derivation shows that the belief states of correlated ads can be naturally updated using a formula similar to collaborative filtering. To test our model, a real world ad dataset from a major search engine is collected and categorized. Experimenting over the data, we provide an analyse of the effect of the underlying parameters, and demonstrate that our algorithms significantly outperform other strong baselines