This book discusses new techniques for detecting, controlling, and exploiting the impacts of temperature variations on nanoscale circuits and systems. A new sensor system is described that can determine the temperature dependence as well as the operating temperature to improve system reliability. A new method is presented to control a circuit's temperature dependence by individually tuning pull-up and pull-down networks to their temperature-insensitive operating points. This method extends the range of supply voltages that can be made temperature-insensitive, achieving insensitivity at nominal voltage for the first time.

Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process.

The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider:

. how a crowd of imperfect decision makers outperforms experts' decisions;

. how to decrease decision makers' imperfection by reducing knowledge available;

. how to decrease imperfection via automated elicitation of DM preferences;

. a human's limited willingness to master the available decision-support tools as an additional source of imperfection;

. how the decision maker's emotional state influences the rationality; a DM support of edutainment robot based on its system of values and respecting emotions.

The book will appeal to anyone interested in the challenging topic of DM theory and its applications.

Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process. The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider: * how a crowd of imperfect decision makers outperforms experts' decisions; * how to decrease decision makers' imperfection by reducing knowledge available; * how to decrease imperfection via automated elicitation of DM preferences; * a human's limited willingness to master the available decision-support tools as an additional source of imperfection; * how the decision maker's emotional state influences the rationality; a DM support of edutainment robot based on its system of values and respecting emotions. The book will appeal to anyone interested in the challenging topic of DM theory and its applications.

This book discusses new techniques for detecting, controlling, and exploiting the impacts of temperature variations on nanoscale circuits and systems. A new sensor system is described that can determine the temperature dependence as well as the operating temperature to improve system reliability. A new method is presented to control a circuit's temperature dependence by individually tuning pull-up and pull-down networks to their temperature-insensitive operating points. This method extends the range of supply voltages that can be made temperature-insensitive, achieving insensitivity at nominal voltage for the first time.

Many complex systems found in nature can be viewed as function optimizers. In particular, they can be viewed as such optimizers of functions in extremely high dimensional spaces. Given the difficulty of performing such high-dimensional op timization with modern computers, there has been a lot of exploration of computa tional algorithms that try to emulate those naturally-occurring function optimizers. Examples include simulated annealing (SA [15,18]), genetic algorithms (GAs) and evolutionary computation [2,3,9,11,20-22,24,28]. The ultimate goal of this work is an algorithm that can, for any provided high-dimensional function, come close to extremizing that function. Particularly desirable would be such an algorithm that works in an adaptive and robust manner, without any explicit knowledge of the form of the function being optimized. In particular, such an algorithm could be used for distributed adaptive control---one of the most important tasks engineers will face in the future, when the systems they design will be massively distributed and horribly messy congeries ofcomputational systems.