Adaptive Probability Theory: Human Biases as an Adaptation

Humans make mistakes in our decision-making and probability judgments. While the heuristics used for decision-making have been explained as adaptations that are both efficient and fast, the reasons why people deal with probabilities using the reported biases have not been clear. We will see that some of these biases can be understood as heuristics developed to explain a complex world when little information is available. That is, they approximate Bayesian inferences for situations more complex than the ones in laboratory experiments and in this sense might have appeared as an adaptation to those situations. When ideas as uncertainty and limited sample sizes are included in the problem, the correct probabilities are changed to values close to the observed behavior. These ideas will be used to explain the observed weight functions, the violations of coalescing and stochastic dominance reported in the literature

Investing in America\u27s Surface Transportation Infrastructure: The Need for a Multi-Year Reauthorization Bill: Hearing Before the S. Comm. on Env\u27t & Pub. Works, 116th Cong., July 10, 2019

The Fourth National Climate Assessment, released in November 2018, described the serious impacts of climate change already being felt throughout the U.S., and made clear that the risks to communities all across the country are growing rapidly. These findings, along with those in the 2018 Intergovernmental Panel on Climate Change (IPCC) report should serve as an immediate call to action. Even if we manage to limit planetary warming to just 2 degrees Celsius, the world will still face increased chances of economic and social upheaval from more severe flooding, droughts, heatwaves, and other climate impacts as well as devastating environmental consequences, the IPCC report warns. The consensus from leading scientific research academies within the United States and internationally is clear: multiple lines of evidence indicate, and have indicated for years, that our atmosphere is warming, sea levels are rising, the magnitude and frequency of certain extreme weather events is increasing, and that human activity is the primary driver of climate change. As described in the IPCC Special Report, the consensus is that countries around the world must rapidly decarbonize their economies, cutting greenhouse gas emissions in half by 2030 and to near zero by 2050. The U.S. Department of Defense, and leaders within the defense and national security communities, have also recognized climate change as a “national security issue” that requires adapting military operations and planning to ensure readiness. Despite our understanding of the consequences we will face and the urgency to act, U.S. GHG emissions from fossil fuel combustion increased by 2.7 percent in 2018, according the Rhodium Group. Clearly more action is needed. While we all recognize the importance of transportation in our daily lives and for our economy, it is also important to recognize that the transportation sector is the largest contributor of GHG emissions in the United States, and is already facing significant impacts from climate change. There is an urgent need, therefore, to transition to a low-carbon and more resilient transportation system. Such a transition would not only reduce emissions and fight climate change, it also would bring additional important benefits, including protecting public health by reducing conventional air pollution, providing more mobility options, and driving innovation and economic growth through policy action and through public and private investment

Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

Congruency sequence effects and previous response times: conflict adaptation or temporal learning?

In the present study, we followed up on a recent report of two experiments in which the congruency sequence effect-the reduction of the congruency effect after incongruent relative to congruent trials in Stroop-like tasks-was observed without feature repetition or contingency learning confounds. Specifically, we further scrutinized these data to determine the plausibility of a temporal learning account as an alternative to the popular conflict adaptation account. To this end, we employed a linear mixed effects model to investigate the role of previous response time in producing the congruency sequence effect, because previous response time is thought to influence temporal learning. Interestingly, slower previous response times were associated with a reduced current-trial congruency effect, but only when the previous trial was congruent. An adapted version of the parallel episodic processing (PEP) model was able to fit these data if it was additionally assumed that attention "wanders" during different parts of the experiment (e.g., due to fatigue or other factors). Consistent with this assumption, the magnitude of the congruency effect was correlated across small blocks of trials. These findings demonstrate that a temporal learning mechanism provides a plausible account of the congruency sequence effect

ACCULTURATION AND POST-IMMIGRATION CHANGES IN OBESITY, PHYSICAL ACTIVITY, AND NUTRITION: COMPARING HISPANICS AND ASIANS IN THE WATERLOO REGION, ONTARIO, CANADA.

OBJECTIVES: The acculturation hypothesis speculates that as new immigrants get exposed to more obesogenic environments, they progressively acquire the unhealthy lifestyles of the host society, and their obesity risk gradually increases since time of arrival. However, the consistency of the presumed acculturation effect across immigrant groups and gender, and the reasons behind individual changes in lifestyle behaviors remain unclear. Thus, this study investigated the acculturation hypothesis in the Canadian context by comparing two foreign groups, Hispanics and East/Southeast Asians, which present contrasting post-settlement obesity patterns and behavioral trends. Methods: A 41-item questionnaire (including open-ended questions) was administered with 100 first-generation immigrants in the K-W Region to gather information on weight-relatedmeasures, acculturation levels, psychological stress, lifestyle behaviors, and perceived causes of changes in diet and physical activity. A logistic regression analysis was performed to estimate the likelihood of being overweight-obese, while interview transcripts were analyzed to identify response themes and explore causal relationships. RESULTS: Hispanics exhibited considerably higher body mass index levels and larger weight gains, and a nearly nine times higher overweight risk than East/Southeast Asians. Overweight risk was also higher for males and less-educated immigrants. Data collected shows that weight gains were larger for newcomers with high average psychological stress scores, and 38% of Hispanic participants mentioned either stress or depression as causes for their weight gains. The acculturation analysis revealed that East/Southeast Asians were significantly less integrated into Canadian society and more likely to maintain their traditional diets, while both groups reportedperceived-increased levels of recreational physical activity, which contradicts the belief of a linear uniform adoption of unhealthy lifestyle behaviors. DISCUSSION: Results support the notion that the impact of duration of residence does vary by ethnicity and gender. Future prevention efforts should focus on the foreign groups most likely to develop obesity, and pay particular attention to less-educated immigrants, who may be more likely to acquire unhealthy habits after settlement. Results also highlight the emergence of acculturative stress as a significant obesity-risk factor, and support the implementation of obesity preventive efforts that help immigrants manage post-settlement-related feelings of anxiety and depression through the inclusion of social integration strategies. In an increasingly diverse and multiethnic Canada, we expect the dissemination of the research findings to help recent and long-term immigrants to become more aware of obesity-relatedissues, and thus facilitate the adoption of healthier lifestyles after settlement in Canada

User-centred design of flexible hypermedia for a mobile guide: Reflections on the hyperaudio experience

A user-centred design approach involves end-users from the very beginning. Considering users at the early stages compels designers to think in terms of utility and usability and helps develop the system on what is actually needed. This paper discusses the case of HyperAudio, a context-sensitive adaptive and mobile guide to museums developed in the late 90s. User requirements were collected via a survey to understand visitors’ profiles and visit styles in Natural Science museums. The knowledge acquired supported the specification of system requirements, helping defining user model, data structure and adaptive behaviour of the system. User requirements guided the design decisions on what could be implemented by using simple adaptable triggers and what instead needed more sophisticated adaptive techniques, a fundamental choice when all the computation must be done on a PDA. Graphical and interactive environments for developing and testing complex adaptive systems are discussed as a further step towards an iterative design that considers the user interaction a central point. The paper discusses how such an environment allows designers and developers to experiment with different system’s behaviours and to widely test it under realistic conditions by simulation of the actual context evolving over time. The understanding gained in HyperAudio is then considered in the perspective of the developments that followed that first experience: our findings seem still valid despite the passed time

Examining How Federal Infrastructure Policy Could Help Mitigate and Adapt to Climate Change: Hearing Before the H. Comm. on Transp. & Infrastructure, 116th Cong., Feb. 26, 2019 (Statement of Vicki Arroyo)

As the Fourth National Climate Assessment, released in November, describes, the United States is already experiencing serious impacts of climate change—and the risks to communities all across the country are growing rapidly. These findings, along with those in the 2018 Intergovernmental Panel on Climate Change (IPCC)report, are clear and should be a call to immediate action. Even if we manage to limit planetary warming to just 2 degrees C, the world will still face increased chances of economic and social upheaval from more severe flooding, droughts, heatwaves, and other climate impacts as well as devastating environmental consequences, the IPCC report warns. The scientific consensus as described in the IPCC Special Report is that countries around the world must rapidly decarbonize their economies, cutting greenhouse gas emissions in half by 2030 and to near zero by 2050. Yet the current trends are going in the wrong direction. Despite our increasing understanding of the narrowing window to act, U.S. GHG emissions increased by 3.4% in 2018, according to a January report from the Rhodium Group. Clearly more action is needed. The encouraging news is that many states and cities have committed to taking action. They are taking steps to reduce emissions through legislation, executive orders, and pledges made in collaborations such as the US Climate Alliance –now covering roughly half the US population and GDP. In my testimony, I will be focusing on the transportation sector, which is the largest contributor of GHG emissions in the United States, and is already facing significant impacts from climate change. Federal standards have been important in increasing efficiency and reducing emissions, yet transportation-sector emissions are increasing as more vehicle miles are driven, more freight is transported in trucks, and airline travel continues to grow. Transportation is becoming an increasingly large share of U.S. economy-wide emissions as the power sector decarbonizes as a result of market shifts and policy. There is an urgent need, therefore, to transition to a low-carbon transportation system. Such a transition would not only reduce emissions and fight climate change, it also would bring additional important benefits, including protecting public health by reducing conventional air pollution, providing more mobility options, and driving innovation and economic growth through policy action and through public and private investment