Embers of autoregression show how large language models are shaped by the problem they are trained to solve.

The widespread adoption of large language models (LLMs) makes it important to recognize their strengths and limitations. We argue that to develop a holistic understanding of these systems, we must consider the problem that they were trained to solve: next-word prediction over Internet text. By recognizing the pressures that this task exerts, we can make predictions about the strategies that LLMs will adopt, allowing us to reason about when they will succeed or fail.

Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures.

People's decisions often deviate from classical notions of rationality, incurring costs to themselves and society. One way to reduce the costs of poor decisions is to redesign the decision problems people face to encourage better choices. While often subtle, these can have dramatic effects on behavior and are increasingly popular in public policy, health care, and marketing.

Resampling reduces bias amplification in experimental social networks.

Large-scale social networks are thought to contribute to polarization by amplifying people's biases. However, the complexity of these technologies makes it difficult to identify the mechanisms responsible and evaluate mitigation strategies. Here we show under controlled laboratory conditions that transmission through social networks amplifies motivational biases on a simple artificial decision-making task.

How do Humans Overcome Individual Computational Limitations by Working Together?

Since the cognitive revolution, psychologists have developed formal theories of cognition by thinking about the mind as a computer. However, this metaphor is typically applied to individual minds. Humans rarely think alone; compared to other animals, humans are curiously dependent on stores of culturally transmitted skills and knowledge, and we are particularly good at collaborating with others.

Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations.

Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distinctive features of human intelligence. In this paper, we show that information accumulation in multigenerational social networks can be produced by a form of distributed Bayesian inference that allows individuals to benefit from the experience of previous generations while expending little cognitive effort.

Factors influencing property selection for conservation revolving funds.

Finding sustainable ways to increase the amount of private land protected for biodiversity is challenging for many conservation organizations. In some countries, organizations use revolving-fund programs, whereby land is purchased and then sold to conservation-minded owners under condition they enter into a conservation covenant or easement. The sale proceeds are used to purchase, protect, and sell additional properties, incrementally increasing the amount of protected private land.