Climate Change, Intergenerational Fairness, and the Promises and Pitfalls of Artificial Intelligence

Abstract Among his many achievements, Daniel Kahneman will be remembered for his fundamental contribution to the economics of fairness with its many far-reaching applications. In this paper, I focus on intergenerational fairness and its importance in tackling climate change, a high-stakes example of an intergenerational dilemma. Drawing on recent technological advances, I explore how artificial intelligence (AI) can be applied to promoting fairness and cooperation in intergenerational dilemmas. An intergenerational dilemma is characterised by three key features: asymmetry in decision power and outcomes, no possibility for reciprocity between generations, and the global nature of the problem. Building on the literature of (contemporary) economic games, I discuss how AI has the potential to change “the rules of the game” by acting as a market participant or a market maker (i.e., social planner). I outline several directions for future research, where applying AI to the problem of intergenerational dilemmas shows promise, including intra- and intergenerational fairness, long-term preferences, and mechanism design. However, while AI holds the potential to help us tackle major societal issues like climate change, I conclude with a cautionary note that excessive use of AI today—even if well-intended to benefit future generations—could further accelerate the very problems, including climate change, that we set out to tackle.

Authors

Institutions

Publication Details

Journal
Environmental and Resource Economics
Published
2025-04-09
DOI
https://doi.org/10.1007/s10640-025-00986-y
Citations
2
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
8.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Climate Change, Intergenerational Fairness, and the Promises and Pitfalls of Artificial Intelligence

Oliver Hauser
2 citations
Environmental and Resource Economics
Evolutionary Game Theory and Cooperation
8.40
article

Climate Change, Intergenerational Fairness, and the Promises and Pitfalls of Artificial Intelligence

Oliver Hauser
article en
2 citations

Abstract

Abstract Among his many achievements, Daniel Kahneman will be remembered for his fundamental contribution to the economics of fairness with its many far-reaching applications. In this paper, I focus on intergenerational fairness and its importance in tackling climate change, a high-stakes example of an intergenerational dilemma. Drawing on recent technological advances, I explore how artificial intelligence (AI) can be applied to promoting fairness and cooperation in intergenerational dilemmas. An intergenerational dilemma is characterised by three key features: asymmetry in decision power and outcomes, no possibility for reciprocity between generations, and the global nature of the problem. Building on the literature of (contemporary) economic games, I discuss how AI has the potential to change “the rules of the game” by acting as a market participant or a market maker (i.e., social planner). I outline several directions for future research, where applying AI to the problem of intergenerational dilemmas shows promise, including intra- and intergenerational fairness, long-term preferences, and mechanism design. However, while AI holds the potential to help us tackle major societal issues like climate change, I conclude with a cautionary note that excessive use of AI today—even if well-intended to benefit future generations—could further accelerate the very problems, including climate change, that we set out to tackle.

Environmental and Resource EconomicsVol. 88(10)
University of Exeter (GB)
Openalex Percentile: Top 4%
Evolutionary Game Theory and Cooperation
8.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.