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As a trained economist and neuroscientist working with computational models of decision making under evolutionary constraints, I am especially interested in the dynamics that govern homeostatic processes that are optimised via overt regulatory behaviour - such as temperature, hydration, and energy regulation, such that experimentally testable predictions can be specified.  
 
As a trained economist and neuroscientist working with computational models of decision making under evolutionary constraints, I am especially interested in the dynamics that govern homeostatic processes that are optimised via overt regulatory behaviour - such as temperature, hydration, and energy regulation, such that experimentally testable predictions can be specified.  
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Revision as of 10:26, 11 May 2015

Complex Systems Summer School 2015

Ebola

The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
2) How/can we use digital data to either improve forecasts or inform model selection?
3) Can one quantify the value of additional information in real-time?
Contact: Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu

Interested:

Homeostatic Dynamics and the Optimality of Behavior

The survival of all organisms is predicated on occupying a small subspace of internal states, the long-run regulation of which is contingent on behaviour. Currently most models of reinforcement learning and decision-making make the assumption that behaviour is optimal insofar as it maximises reward acquisition by maximising the expectation value of reward. An often unchallenged assumption of this approach is that the target variable to be maximized is an ergodic observable. An ergodic observable is characterised by the time-average converging to the expectation value. Recent work by Peters and co-workers on dynamics in decision making [1] [2] show that the underlying dynamics of a process should govern the objective function that is optimised; the expectation operator for purely additive dynamics and the time average for purely multiplicative dynamics.

In this project I will ask two questions: First, what are the characteristic dynamics of homeostatic variables? Second, how do these dynamics constrain the objective function that biological agents must maximise? I will investigate the degree to which such dynamics are ergodic, or not. Non-ergodic processes are likely common in homeostatic systems. For instance, reaction rates of biochemical networks typically grow by a constant multiplicative factor for every stepwise change in core temperature. Any biological agent engaging in behavioural thermoregulation of such products thus faces multiplicative dynamics, and as such according to the framework should maximise time average growth, not the expectation value. I will survey extant literatures on homeostatic systems, looking for cases in which the underlying dynamics are clearly characterised, and for which there is a plausible and unambiguous path to how such a system can be behaviourally regulated.

As a trained economist and neuroscientist working with computational models of decision making under evolutionary constraints, I am especially interested in the dynamics that govern homeostatic processes that are optimised via overt regulatory behaviour - such as temperature, hydration, and energy regulation, such that experimentally testable predictions can be specified.

Interested:

Contact: tobiasm@drcmr.dk

[1] O. Peters and M. Gell-Mann, “Evaluating gambles using dynamics,” arXiv.org. 2014.
[2] O. Peters, “The time resolution of the St Petersburg paradox,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 369, no. 1956, pp. 4913–4931, Oct. 2011.