Difference between revisions of "Complex Systems Summer School 2015-Projects & Working Groups"
From Santa Fe Institute Events Wiki
(→Preliminary Discussion Meetings)
(→Preliminary Discussion Meetings)
|Line 104:||Line 104:|
2pm in the coffee shop
2pm in the coffee shop
Revision as of 06:00, 10 June 2015
- 1 Ebola
- 2 Homeostatic Dynamics and the Optimality of Behavior
- 3 Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe
- 4 Preliminary Discussion Meetings
- 4.1 Exchange-Company Networks (email@example.com)
- 4.2 Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe (Melissa - firstname.lastname@example.org)
- 4.3 Network Analysis of Arxiv (Daniel - email@example.com)
- 4.4 Scaling effects in bodies, communities, ecosystems (Cobain - firstname.lastname@example.org)
- 4.5 Dynamics of homicide (Matthew Ingram - email@example.com)
- 4.6 Organ Transplant (Christine - firstname.lastname@example.org)
- 4.7 City resilience // Evolutionary stable states in trees (Richard - email@example.com)
- 4.8 Modeling brain diseases (Sahil - firstname.lastname@example.org)
- 4.9 Analysis of UK parliament speeches 1935-2014 (Stefano - email@example.com)
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 - firstname.lastname@example.org
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   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.
 O. Peters and M. Gell-Mann, “Evaluating gambles using dynamics,” arXiv.org. 2014.
 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.
Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe
Many communities in Africa have been surprisingly resilient in the face of a host of devastating challenges. The people of Mazvihwa Communal Area in Zimbabwe have lived through more than a century of rapid change through the colonial, liberation war, and post-colonial periods. There have been dramatic changes in public health (ranging from better control of communicable diseases after World War II, to child vaccination programs after independence, to the AIDS pandemic especially from the mid-1990s to the end of the 2000s) and in land access and use (with repeated removals, resistance, and returns of communities to land designated for white settlement). These shifts in population distribution have interacted with rapid natural increase in population (especially in the period 1950-1990) driven by high fertility and declining mortality; followed by recent decades of declining fertility and high AIDS-related mortality. Differences in religious beliefs mean that these changes are uneven across households and areas. The country's economy has meanwhile gone through a series of long cycles of boom and busts, and during the 2000s experienced inflation reaching a billion billion billion per cent.
The Muonde Trust is a Zimbabwean non-governmental organization established to help support the community in Mazvihwa to continue developing and deploying bottom-up solutions in response to these challenges. Mazvihwa has a semi-arid subtropical climate with remnant woodlands and a combination of largely subsistence agriculture and livestock production. From the point of view of most of the people in Mazvihwa, and as taken up by the community network of the Muonde Trust, the “sustainability” of their area now requires a series of linked changes in land use and investments in natural capital.
Data and Questions
The data we have on this community and ecosystem originates from an ongoing community-based participatory research project originally begun in the 1980s and since continued by the Muonde Trust. It includes robust quantitative data on human demography, health, nutrition, agricultural practices, rainfall, land use choices, woodland dynamics, household assets, and land tenure. Our goal at SFI is to develop theoretical or simulation studies which would help us to better understand the resilience and sustainability of this system, which would eventually be informed by the data. Questions we might address using complex systems methods include:
1) How do individuals and resources flow through households and communities? (Empirical data shows that the composition of households changes rapidly, even though most analyses of these societies tends to assume they are static and natural units of analysis). It is clear that individuals are variously strategizing through households as well as within other kin, religious and clan groups. At the same time households also have emergent properties. In contexts of rapidly shifting demography and changing resource access, are there ways that we can use network analysis to illuminate these complexities?
2) How best can community as a whole allocate their land to agriculture, pasture, and woodland when these components interact and feedback to each other? One of the main land-use decisions facing the community is the trade-off between agricultural cultivation (which requires fencing to keep out livestock as well as water harvesting techniques) and retaining woodland areas that have cultural value as well as providing grazing space and forage for livestock (and many other economic benefits). This relationship is complex, with livestock providing benefits to agriculture (manure for fertilizer and draft power for cultivation), and vice versa (well-tended fields provide considerable feed for livestock). The community derives benefits from all these land uses, including food for subsistence from agriculture, meat and milk from livestock, and cultural values and a wide variety of benefits from woodland (including fuelwood, construction materials, a variety of foods and medicines, and improved soil characteristics). In addition, community members may sell livestock, as well as using them for bridewealth and compensation in the case of some deaths. How can this system be represented and manipulated in a model to create optimal strategies for the well-being of the system?
Our methodology is open to what we learn during the summer school, but some ideas include: network analysis to study the way people and resources connect and flow through the households and other components of the system; an analytical mathematical model of the interacting components of the system, e.g. coupled differential equations; cellular automata which can represent the land use category of each part of a farmer's land and underlie a decision support tool.
Preliminary Discussion Meetings
Exchange-Company Networks (email@example.com)
10:45am in the coffee shop
Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe (Melissa - firstname.lastname@example.org)
10:45am in the senior common room (the room behind our lecture hall)
Network Analysis of Arxiv (Daniel - email@example.com)
10:45am in the lecture hall
Also interested in combining multiplex/multilayer networks
Scaling effects in bodies, communities, ecosystems (Cobain - firstname.lastname@example.org)
11:30am in the coffee shop
Also tying in prehistoric hunting populations
Dynamics of homicide (Matthew Ingram - email@example.com)
Integrating temporal, spatial, and multi-level concepts
1:30pm in the coffee shop
Organ Transplant (Christine - firstname.lastname@example.org)
2pm in the coffee shop
City resilience // Evolutionary stable states in trees (Richard - email@example.com)
3pm in the coffee shop
Modeling brain diseases (Sahil - firstname.lastname@example.org)
No meeting time indicated
Analysis of UK parliament speeches 1935-2014 (Stefano - email@example.com)
No meeting time indicated