Complex Systems Summer School 2015-Projects & Working Groups
From Santa Fe Institute Events Wiki
- 1 Ebola
- 2 Homeostatic Dynamics and the Optimality of Behavior
- 3 Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe
- 4 Mapping Complexity/Human Knowledge as a Complex Adaptive System
- 5 Preliminary Discussion Meetings
- 5.1 Exchange-Company Networks (firstname.lastname@example.org)
- 5.2 Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe (Melissa - email@example.com)
- 5.3 Network Analysis of Arxiv (Daniel - firstname.lastname@example.org)
- 5.4 Ebola virus disease spread (Junming - email@example.com)
- 5.5 Scaling effects in bodies, communities, ecosystems (Cobain - firstname.lastname@example.org)
- 5.6 Dynamics of homicide (Matthew Ingram - email@example.com)
- 5.7 Organ Transplant (Christine - firstname.lastname@example.org)
- 5.8 Multi-dimensional social networks in the evolution, development and resilience of informal economies
- 5.9 City resilience // Evolutionary stable states in trees (Richard - email@example.com)
- 5.10 Modeling brain diseases (Sahil - firstname.lastname@example.org)
- 5.11 Analysis of UK parliament speeches 1935-2014 (Stefano - email@example.com)
- 5.12 Mapping Complexity/Human Knowledge as a Complex Adaptive System (firstname.lastname@example.org)
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 - email@example.com
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.
Mapping Complexity/Human Knowledge as a Complex Adaptive System
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
Data and Questions
The goals of this project include
- Extract the terms from approximately 1600 working papers published by SFI
- Map the intra/inter conceptual network structures
- Study the evolution of these structures across time
- High-light the gap-closure of knowledge reverse-salients (if any)
- Capture any of the network patterns that repeat
- Study the diffusion of concepts across the network
- Provide visualization tools for navigating the complexity corpus, etc
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
- SFI Working Papers: http://www.santafe.edu/research/working-papers/
- Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459
- Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas
- Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275
- Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE
- Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM
- LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/
- What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf
- LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis
- LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm
- John Thomas (firstname.lastname@example.org)
- Laura Condon (email@example.com)
- Haitao Shang (firstname.lastname@example.org)
- Sharon Greenblum (email@example.com)
- Christopher Verzijl (firstname.lastname@example.org)
- Nilton Cardoso (email@example.com)
- Glenn Magerman (firstname.lastname@example.org)
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
Ebola virus disease spread (Junming - firstname.lastname@example.org)
11:00am in the coffee shop
Scaling effects in bodies, communities, ecosystems (Cobain - email@example.com)
11:30am in the coffee shop
Also tying in prehistoric hunting populations
Dynamics of homicide (Matthew Ingram - firstname.lastname@example.org)
Integrating temporal, spatial, and multi-level concepts
1:30pm in the coffee shop
Organ Transplant (Christine - email@example.com)
2pm in the coffee shop
2:00pm in the lecture hall
Eloy & Carolina - firstname.lastname@example.org, email@example.com
City resilience // Evolutionary stable states in trees (Richard - firstname.lastname@example.org)
3pm in the coffee shop
Modeling brain diseases (Sahil - email@example.com)
No meeting time indicated
Analysis of UK parliament speeches 1935-2014 (Stefano - firstname.lastname@example.org)
No meeting time indicated
Mapping Complexity/Human Knowledge as a Complex Adaptive System (email@example.com)
1:30pm/Wed 6/10 in Conference Room (Tentative)