Simon Carrignon

Name:	Simon Carrignon
Affiliation:	McDonald Institute for Archaeological Research, Univeristy of Cambridge
E-mail:	(only provided to users who are logged into talks.cam)
Last login:	13 Sep 2024, 4:59 p.m.

Public lists managed by Simon Carrignon

Talks given by Simon Carrignon

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Talks organised by Simon Carrignon

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• Survival in the Face of Adversity: Exploring Human Cooperation in the Paleolithic of Kazakhstan through an Agent-Based Model
• Computational and machine learning methods to investigate the complex human-environment interaction. From the Mediterranean to the Andes
• Networks and Resilience – The impact of connectivity on ancient societies
• Graves, grains and grievances: how quantifying archaeological data can help us trace urban dynamics against their global context (Joint with Garrod)
• Modelling land use dynamics and responses to climate variability in late antique Basel, Switzerland
• Graves, grains and grievances: how quantifying archaeological data can help us trace urban dynamics against their global context
• Harvesting Proxies: The ecological niche for rice in Yayoi period Japan via multi-proxy species distribution modelling
• Analysis of spatio-temporal changes in the ecological niches of major domesticated crops in China: application of Species Distribution Modelling'
• A new set of Bayesian modelling tools for compositional legacy data in archaeology: the case study of Muisca metalwork from Colombia (AD 600-1600)
• Underexplored frontiers: modelling the archaeological potential in maritime contexts. Utility, challenges and misconceptions
• SagaScape: Simulating human impact and community formation in southwest Anatolia during the first millennium BCE
• Layering computational techniques for the analysis of non-urban communities in hillfort sites
• The chronology of cultural, agricultural and demographic change in ancient Sāmoa indicates that population rise is the proximate origin of Polynesian chiefdoms
• Mixing apples and oranges: Integrating Bayesian confirmation and classification algorithms to combine incompatible, high dimensional data
• Mixing apples and oranges: Integrating Bayesian confirmation and classification algorithms to combine incompatible, high dimensional data