P J Lamberson
Biographic Data
| ID | 1729579 |
|---|---|
| NAME | P J Lamberson |
| GIVEN NAMES | P J |
| FAMILY NAME | Lamberson |
| SIGNATURE | LAMBERSON P J |
| AFFILIATIONS | University of California System |
| ORCID | 0000-0002-4176-9428 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 27 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Political polarization as a co-adaptive process
Political polarization is often characterized as a consequence of changes in media content or technology. We argue, in contrast, for an account that views polarization in media and the public as a co-adaptive process. This paper begins with a brief review of cultural evolution and co-adaptation and then considers the application of similar ideas to changes in media and the public over time. Using formal models, we suggest that—in combination with…
Network games with local correlation and clustering
In many contexts, an individual’s beliefs and behavior are affected by the choices of their social or geographic neighbors. This influence results in local correlation in people’s actions, which in turn affects how information and behaviors spread. Previously developed frameworks capture local social influence using network games, but discard local correlation in players’ strategies. This paper develops a network games framework that allows for l…
A Model of Attentiveness to Outlying News
This paper offers a formal-theoretical account for the preponderance of negative news content. It draws on work suggesting that humans and news media prioritize outlying information and argues that one reason for a negativity bias in news consumption and production is that negative information is more “outlying”: that is, further away from expectations. Expectations change over time, however, and so too do negativity (and other) biases. This dyna…
Diffusion in Networks
This chapter examines models of diffusion in networks, and specifically how the topology of the network impacts the spreading process. The chapter begins by discussing epidemiological models and how stochastic dominance relations can be used to understand the effect of the degree distribution of the network. The chapter then turns to more sophisticated models of social influence, including threshold models and models of social learning. A key ins…
Leveraging social influence to address overweight and obesity using agent-based models
Essay
This paper formally defines tipping points as discontinuities between current and future states of a system and introduces candidate measures of when a system tips based on changes in the probability distribution over future states. We make two categorical distinctions between types of tips relevant in social contexts: The first differentiates between direct tips and contextual tips. A direct tip occurs when a gradual change in the value of a var…
Social Learning in Social Networks
Leveraging social influence to address overweight and obesity using agent-based models
Essay
This paper formally defines tipping points as discontinuities between current and future states of a system and introduces candidate measures of when a system tips based on changes in the probability distribution over future states. We make two categorical distinctions between types of tips relevant in social contexts: The first differentiates between direct tips and contextual tips. A direct tip occurs when a gradual change in the value of a var…
Social Learning in Social Networks
Essay
This paper formally defines tipping points as discontinuities between current and future states of a system and introduces candidate measures of when a system tips based on changes in the probability distribution over future states. We make two categorical distinctions between types of tips relevant in social contexts: The first differentiates between direct tips and contextual tips. A direct tip occurs when a gradual change in the value of a var…
Leveraging social influence to address overweight and obesity using agent-based models
Diffusion in Networks
This chapter examines models of diffusion in networks, and specifically how the topology of the network impacts the spreading process. The chapter begins by discussing epidemiological models and how stochastic dominance relations can be used to understand the effect of the degree distribution of the network. The chapter then turns to more sophisticated models of social influence, including threshold models and models of social learning. A key ins…
A Model of Attentiveness to Outlying News
This paper offers a formal-theoretical account for the preponderance of negative news content. It draws on work suggesting that humans and news media prioritize outlying information and argues that one reason for a negativity bias in news consumption and production is that negative information is more “outlying”: that is, further away from expectations. Expectations change over time, however, and so too do negativity (and other) biases. This dyna…
Network games with local correlation and clustering
In many contexts, an individual’s beliefs and behavior are affected by the choices of their social or geographic neighbors. This influence results in local correlation in people’s actions, which in turn affects how information and behaviors spread. Previously developed frameworks capture local social influence using network games, but discard local correlation in players’ strategies. This paper develops a network games framework that allows for l…
Political polarization as a co-adaptive process
Political polarization is often characterized as a consequence of changes in media content or technology. We argue, in contrast, for an account that views polarization in media and the public as a co-adaptive process. This paper begins with a brief review of cultural evolution and co-adaptation and then considers the application of similar ideas to changes in media and the public over time. Using formal models, we suggest that—in combination with…
Opinion Dynamics and Social Influence (4 works) · Computer Science (3 works) · Sociology (3 works) · Complex Network Analysis Techniques (2 works) · Economics (2 works) · Psychology (2 works) · Social media (2 works) · Social Psychology (2 works) · Artificial Intelligence (1 work) · Behavioral Health and Interventions (1 work)