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Wenzel Anton von Kaunitz-Rittberg is generally recognized as the principal architect of the "Diplomatic Revolution" of 1756 and as one of the greatest statesmen of his day, yet there have been curiously few attempts to describe and analyze the process by which he rose to such eminence. In the available historical literature Kaunitz usually first appears on the scene at the peace congress of Aix-la-Chapelle (1748), although there are occasional references to his earlier diplomatic career. Nineteenth-century historians assumed that Kaunitz's experiences at Aix-la-Chapelle provided the insight which finally led him to propose the reversal of alliances. Adolph Beer, for example, asserted that Kaunitz's enmity towards England was born at Aix-la-Chapelle and that the policy of 1755 first entered his mind in 1748. Though twentieth-century historians have tended to de-emphasize the revolutionary nature of the reversal of alliances and so have been less willing to accept the easy equation that Aix-la-Chapelle produced the diplomatic change of 1756, there has been no systematic examination of the development of the new diplomatic policy in Kaunitz's own mind. While it might be said that the development of that policy represents merely the ordinary maturation of a diplomat, the growth of understanding, the fact is that few of his contemporaries possessed such understanding. It therefore seems of some value to know how this master diplomat matured.

The State of Texas leads the other states of the nation in the num ber of major disasters: it is first in tornadoes and devastating floods and second in hurricanes. This article describes how Texas, under its Civil Protection Act of 1951, without setting up an independent state agency, has gone about mobiliz ing and utilizing the resources of the state in time of major disaster. The "Texas Plan" is discussed in detail and attention paid in particular to its cost and financing, the planning of disaster relief, preparedness, and training.—Ed.

The mental models approach, a leading strategy to develop risk communications, involves a time‐ and labor‐intensive interview process and a lengthy questionnaire to elicit group‐level risk perceptions. We propose that a similarity ratings approach for structural knowledge elicitation can be adopted to assist the risk mental models approach. The LinkIT game, inspired by games with a purpose (GWAP) technology, is a ludic elicitation tool designed to elicit group understanding of the relations between risk factors in a more enjoyable and productive manner when compared to traditional approaches. That is, consistent with the idea of ludic elicitation, LinkIT was designed to make the elicitation process fun and enjoyable in the hopes of increasing participation and data quality in risk studies. Like the mental models approach, the group mental model obtained via the LinkIT game can hence be generated and represented in a form of influence diagrams. In order to examine the external validity of LinkIT, we conducted a study to compare its performance with respect to a more conventional questionnaire‐driven approach. Data analysis results conclude that the two group mental models elicited from the two approaches are similar to an extent. Yet, LinkIT was more productive and enjoyable than the questionnaire. However, participants commented that the current game has some usability concerns. This presentation summarizes the design and evaluation of the LinkIT game and suggests areas for future work.

Awareness of a small business neighborhood was studied by driving 40 college students through a 1.7-mile route. Twenty other students received no ride. Half of each group had a high frequency of previous exposure to the area (mean number of times in the area = 159.9) and the other half had low frequency (mean = 5.3). The ride significantly increased the number of items mentioned in a free recall task and the number of pictures positively identified in a recognition task. Previous exposure to the area affected only recognition. The kinds of things recalled and recognized are discussed in terms of a utilitarian versus aesthetic orientation to the environment.

This article proposes a quantitative risk assessment and management framework that supports strategic asset‐level resource allocation decision making for critical infrastructure and key resource protection. The proposed framework consists of five phases: scenario identification, consequence and criticality assessment, security vulnerability assessment, threat likelihood assessment, and benefit‐cost analysis. Key innovations in this methodology include its initial focus on fundamental asset characteristics to generate an exhaustive set of plausible threat scenarios based on a target susceptibility matrix (which we refer to as asset‐driven analysis) and an approach to threat likelihood assessment that captures adversary tendencies to shift their preferences in response to security investments based on the expected utilities of alternative attack profiles assessed from the adversary perspective. A notional example is provided to demonstrate an application of the proposed framework. Extensions of this model to support strategic portfolio‐level analysis and tactical risk analysis are suggested.

This article develops a quantitative all‐hazards framework for critical asset and portfolio risk analysis (CAPRA) that considers both natural and human‐caused hazards. Following a discussion on the nature of security threats, the need for actionable risk assessments, and the distinction between asset and portfolio‐level analysis, a general formula for all‐hazards risk analysis is obtained that resembles the traditional model based on the notional product of consequence, vulnerability, and threat, though with clear meanings assigned to each parameter. Furthermore, a simple portfolio consequence model is presented that yields first‐order estimates of interdependency effects following a successful attack on an asset. Moreover, depending on the needs of the decisions being made and available analytical resources, values for the parameters in this model can be obtained at a high level or through detailed systems analysis. Several illustrative examples of the CAPRA methodology are provided.