Christopher G Lucas
Biographic Data
| ID | 1940613 |
|---|---|
| NAME | Christopher G Lucas |
| GIVEN NAMES | Christopher G |
| FAMILY NAME | Lucas |
| SIGNATURE | LUCAS C G |
| AFFILIATIONS | University of Edinburgh |
| ORCID | 0000-0002-6655-8627 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 35 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
A Rational Framework for Group-Based Selective Social Learning
Social learning can be a powerful tool, allowing us to acquire knowledge and adaptive behaviours while bypassing many of the costs of learning through direct experience. However, not everyone’s behaviour is equally valuable to learn from, as other people’s goals or preferences may differ dramatically from our own. In this paper, we consider the problem of selectively learning from others on the basis of direct and indirect inferences about their …
Lossy encoding of distributions in judgment under uncertainty
People often make judgments about uncertain facts and events, for example 'Germany will win the world cup'. Judgment under uncertainty is often studied with reference to a normative ideal according to which people should make guesses that have a high probability of being correct. According to this normative ideal, you should say that Germany will win the world cup if you think that Germany is in fact likely to win. We argue that in many cases, ju…
Naïve information aggregation in human social learning
To glean accurate information from social networks, people should distinguish evidence from hearsay. For example, when testimony depends on others' beliefs as much as on first-hand information, there is a danger of evidence becoming inflated or ignored as it passes from person to person. We compare human inferences with an idealized rational account that anticipates and adjusts for these dependencies by evaluating peers' communications with respe…
A model of conceptual bootstrapping in human cognition
To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations…
What's new? A comprehension bias in favor of informativity
The Balance‐Sample Size Frontier in Matching Methods for Causal Inference
We propose a simplified approach to matching for causal inference that simultaneously optimizes balance (similarity between the treated and control groups) and matched sample size. Existing approaches either fix the matched sample size and maximize balance or fix balance and maximize sample size, leaving analysts to settle for suboptimal solutions or attempt manual optimization by iteratively tweaking their matching method and rechecking balance.…
The Balance‐Sample Size Frontier in Matching Methods for Causal Inference
We propose a simplified approach to matching for causal inference that simultaneously optimizes balance (similarity between the treated and control groups) and matched sample size. Existing approaches either fix the matched sample size and maximize balance or fix balance and maximize sample size, leaving analysts to settle for suboptimal solutions or attempt manual optimization by iteratively tweaking their matching method and rechecking balance.…
A model of conceptual bootstrapping in human cognition
To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations…
The Balance‐Sample Size Frontier in Matching Methods for Causal Inference
We propose a simplified approach to matching for causal inference that simultaneously optimizes balance (similarity between the treated and control groups) and matched sample size. Existing approaches either fix the matched sample size and maximize balance or fix balance and maximize sample size, leaving analysts to settle for suboptimal solutions or attempt manual optimization by iteratively tweaking their matching method and rechecking balance.…
What's new? A comprehension bias in favor of informativity
A model of conceptual bootstrapping in human cognition
To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations…
Naïve information aggregation in human social learning
To glean accurate information from social networks, people should distinguish evidence from hearsay. For example, when testimony depends on others' beliefs as much as on first-hand information, there is a danger of evidence becoming inflated or ignored as it passes from person to person. We compare human inferences with an idealized rational account that anticipates and adjusts for these dependencies by evaluating peers' communications with respe…
A Rational Framework for Group-Based Selective Social Learning
Social learning can be a powerful tool, allowing us to acquire knowledge and adaptive behaviours while bypassing many of the costs of learning through direct experience. However, not everyone’s behaviour is equally valuable to learn from, as other people’s goals or preferences may differ dramatically from our own. In this paper, we consider the problem of selectively learning from others on the basis of direct and indirect inferences about their …
Lossy encoding of distributions in judgment under uncertainty
People often make judgments about uncertain facts and events, for example 'Germany will win the world cup'. Judgment under uncertainty is often studied with reference to a normative ideal according to which people should make guesses that have a high probability of being correct. According to this normative ideal, you should say that Germany will win the world cup if you think that Germany is in fact likely to win. We argue that in many cases, ju…
Computer Science (6 works) · Psychology (5 works) · Artificial Intelligence (4 works) · Cognitive psychology (4 works) · Artificial Intelligence (3 works) · Child and Animal Learning Development (2 works) · Cognition (2 works) · Cognitive science (2 works) · Econometrics (2 works) · Evolutionary Game Theory and Cooperation (2 works)