A longitudinal study of children`s developing knowledge and reasoning in science

The growth in science understanding and reasoning of 12 children is being traced through their primary school years. The paper reports findings concerning children’s growing understandings of evaporation, and their changing responses to exploration activities, that show the complexity and coherence of learning pathways. Children’s responses to identical explorations of flight, separated by two years, are used to explore the interactions between conceptual knowledge and scientific reasoning, and the manner in which they change over this time. The paper discusses the particular insights afforded by a longitudinal study design, and some attendant methodological issues.

Probabilistic reasoning of inconsistent belief in protocol analysis

Security protocols have been recently found with subtle flaws due to incomplete or ambiguous specification. Although formal methods have remarkably assisted in protocol analysis, they ignores the effect of hostile/uncertain environment, which might lead to inconsistent belief that can be held by principals in delivered messages. This discrepant belief may prevent us from representing the insecurity and uncertainty in a real trading situation. Unfortunately, the current approaches lack the ability to handle the inconsistent belief. This article presents a probabilistic method, which intuitively measures the belief from different principals that can be put on the goal of the protocol. The experiments demonstrate our method is useful to enhance the protocol analysis.

Survival misperception, time inconsistency, and implications for life-cycle saving and welfare

Some empirical studies firmly reveal that people tend to form overly pessimistic survival expectations for relatively less distant ages and overly optimistic survival expectations for relatively more distant ages. We incorporate this observation into a life-cycle continuous time overlapping-generations model of consumption/saving with a general form for a subjective survival function. Resulting time-inconsistent optimal control problem has been analytically solved. At the micro level, time inconsistency leads to higher consumption at young and old ages, but this alone fails to improve lifetime well-being since micro-level decisions made with a lack of information about true mortality are suboptimal. In general equilibrium, however, such time inconsistent behavior with survival misperception is conducive to aggregate capital accumulation and greater equilibrium bequest income. The latter effects can produce substantial welfare gains. We also note that empirically observed old age optimistic bias is an important phenomenon, as it helps to avoid unrealistic very old-age debt accumulation within a life-cycle model. In addition, if for a given level of optimistic bias we increase early-life pessimism, this would result in slower capital accumulation, lower bequest income, and thus be detrimental to welfare. Since recent literature reports that young-age survival pessimism has grown over time, it raises some concerns.

Ontology-based automated support for goal–use case model analysis

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. To ensure the quality of such modeled artifacts, a detailed model analysis needs to be performed. However, current requirements engineering approaches generally lack reliable support for automated analysis of consistency, correctness and completeness (3Cs problems) between and within goal models and use case models. In this paper, we present a goal–use case integration framework with tool support to automatically identify such 3Cs problems. Our new framework relies on the use of ontologies of domain knowledge and semantics and our goal–use case integration meta-model. Moreover, functional grammar is employed to enable the semiautomated transformation of natural language specifications into Manchester OWL Syntax for automated reasoning. The evaluation of our tool support shows that for representative example requirements, our approach achieves over 85 % soundness and completeness rates and detects more problems than the benchmark applications.