Making Choices in the Early Mission Period

Portugal has port, Spain has sherry, Sicily has Marsala –– and California has angelica. Angelica is California's original wine: The intensely sweet, fortified dessert cordial has been made in the state for more than two centuries –– primarily made from Mission grapes, first brought to California by the Spanish friars. Angelica was once drunk in vast quantities, but now fewer than a dozen vintners make angelica today. These holdouts from an earlier age are each following a personal quest for the real. For unlike port and sherry, which have strict rules about their production, angelica never gelled into something so distinct that connoisseurs can say, ““This is angelica. This is not.”” This piece looks at the history of the drink, its foggy origins in the Mission period and on through angelica's heyday and down to its degeneration into a staple of the back-alley wino set. Several current vintners are profiled, and they suggest an uncertain future for this cordial.

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Reasonable Bounds on Rationality

10.31234/osf.io/35rej ◽

2020 ◽

Author(s):

Igor Grossmann ◽

Richard Eibach

Keyword(s):

Decision Making ◽

Bounded Rationality ◽

Cognitive Resources ◽

Previous Theory ◽

Making Choices ◽

Points Of View ◽

Sound Judgment

Previous theory and research on bounded rationality has emphasized how limited cognitive resources constrain people from making utility maximizing choices. This paper expands the concept of bounded rationality to consider how people’s rationality may be constrained by their internalization of a qualitatively distinct standard for sound judgment, which is commonly labeled reasonableness. In contrast to rationality, the standard of reasonableness provides guidance for making choices in situations that involve balancing incommensurable values and interests or reconciling conflicting points-of-view. We review recent evidence showing that laypeople readily recognize the distinctions between rationality and reasonableness and thus are able to utilize these as distinct standards to inform their everyday decision-making. The fact that people appear to have internalized rationality and reasonableness as distinct standards of sound judgment supports the notion that people’s pursuit of rationality may be bounded by their determination to also be reasonable.

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ASL Acquisition Principles

10.1093/oso/9780198793816.003.0002 ◽

2017 ◽

Author(s):

Michael Chappell ◽

Bradley MacIntosh ◽

Thomas Okell

Keyword(s):

Arterial Spin Labeling ◽

Spin Labeling ◽

Perfusion Mri ◽

Making Choices ◽

Technical Details

A variety of acquisition methods for arterial spin labeling (ASL) perfusion MRI exist, often with different names referring to broadly similar approaches. This chapter outlines the main flavors of ASL in common use, along with their strengths and weaknesses, before finally presenting the current consensus of the community on good acquisition parameters for general use. The chapter considers the choice of labeling (e.g. pseudo-continuous versus pulsed), readout (e.g. 2D versus 3D), the choice of post-label delay, and a number of other solutions to correct for artifacts such as arterial contamination. Avoiding extensive technical details, this chapter seeks to inform the ASL user when making choices about acquisition parameters for the use of ASL and also provides enough terminology to help a new user understand the key parameters that they need to know when presented with a new ASL dataset.

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Psychological Predictors for Watching Television: The Role of Racial Representation

Psychological Reports ◽

10.1177/00332941211025266 ◽

2021 ◽

pp. 003329412110252

Author(s):

Jennifer Archer ◽

Kadie R. Rackley ◽

Susan Broyles Sookram ◽

Hien Nguyen ◽

Germine H. Awad

Keyword(s):

Undergraduate Students ◽

Social Dominance Orientation ◽

Racial Ideology ◽

Psychological Predictors ◽

Ethnic Representation ◽

Racial Representation ◽

Television Shows ◽

Making Choices ◽

Race Ethnicity

This study explored psychological predictors that may impact viewers’ decision to watch television shows on the basis of perceived racial or ethnic representation. 1998 undergraduate students selected from a list of motivations for watching television that included race-specific motivations such as “a character is of my race/ethnicity.” Participants also completed attitudinal measures of colorblind racial ideology, social dominance orientation, ethnic identity, and ethnic stigma consciousness. Analysis revealed that prejudicial beliefs predicted less salience for racial representation when making choices about television watching, while deeper connection to one’s ethnic group predicted greater salience for representation when making these choices.

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Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

Proceedings of the ASIL Annual Meeting ◽

10.1017/amp.2021.47 ◽

2020 ◽

Vol 114 ◽

pp. 242-245

Author(s):

Jootaek Lee

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Deep Learning ◽

Language Processing ◽

Human Reasoning ◽

The Past ◽

Making Choices ◽

Data Capturing ◽

Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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