Complex Systems Summer School 2014-Project Presentations

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Presentation Notes

Towards a Unified Theory of Biodiversity [slides]

Group members:

• what determines species diversity?
• neutral theory (death rate, birth rate, speciation rate same across all species)
• metabolic theory (all three assumptions are wrong, scaling laws)
• their model: define rates using only one free variable
• can get some qualitatively similar scaling to metabolic theory by limiting energy in system by preventing some births

Software and Living Systems: In Evolution a Software Engineer? [slides]

Group members:
Fahad, Renske, Diana, George, Ana Maria, Stojan and Ernest

• cycle: innovation <-> (constrained by) conservation <-> efficiency
• Hox genes (segmentation of body parts): modular control (each piece corresponds to a different body segment), order is preserved, innovation through duplication of entire cluster
• telecom system: modular, core features are conserved, duplication used for testing etc. which can lead to innovation
• future: how can they inform and guide us

Information Theory of the Heart [slides]

Group members:

• model electricity flow through the heart using two models (probabilistic CA and deterministic PDE) on a 128x128 grid of heart cells
• four wave patterns: point stimulation, spiral, anatomical re-entry, multiple wavelets
• entropy of single cells can't differentiate
• mutual information can identify sets of cells whose excitation times overlap
• future work: transfer entropy
• Q: comment: Granger causality is a more general version of transfer entropy and may be more effective for numerical rather than symbolic data

Tradeoff between Division of Labor and Robustness to Perturbations [slides]

Group members:

• optimum depends on environment, focus on microbial ecology
• assumptions: leaky functions, adaptive gene loss
• "black queen": for each essential task, somebody's got to do it
• threshold phenomenon: when disconnected, more robust; when giant connected component, could make system very vulnerable to environmental changes
• real-world data, then agent-based simulations
• shocking system removes high-degree nodes
• future: explore other systems

The Architecture of an Empirical Genotype-Phenotype Mapping [slides]

Group members:

• three goals:
  • explore internal structure of genotype network
  • explore similarities between genotype networks for different species
  • understand biologically-inspired context
• link between two binding sites if they differ by a point or shift mutation
• findings
  • short geodesics but large diameter
  • positive assortativity of node degree and level of mutation
  • networks studied have nearly optimal accessibility (measures reversibility of evolution)

Are Ants Urban Planners? [slides]

Group members:

• qualitative similarities between ants foraging patterns and city transportation networks
• questions
  • underlying mechanisms
  • different kinds of networks (ant foraging vs ant travel network between nests)
• hypotheses:
  • simple foraging model can produce properties common to both city and ant networks
    • balance energy use with benefits from exploited resources (cost-benefit)
  • a few basic model parameters can describe some of the major qualitative differences (e.g. bottom-up vs top-down)
• simulations using two models (agent-based and global) on multiple scenarios (e.g. lake, river)
• future: explore impact of varied terrain, what is the role of central planning
• Q: can this be used to suggest likely archaeological sites to explore next?

Structural and Functional Robustness in Networks [slides]

Group members:

• ability to retain function in the presence of variation
• three models: sandpile, road network, social organization
  • sandpile: probability of cascade failure
  • road model: two road types, extra cost to switch; effect of removal on simplest path or shortest path
  • social org: "failure" is reduction of performance rather than node removal
• future: consider effect of relational cohesion

A Complex Fishery System in Northern Cameroon [slides]

Group members:

• complex social-ecological system; messy, scarse, quantitative and qualitative data
• focal points:
  • understand dynamics of canal proliferation
  • understand decision-making process
• approaches:
  • game theory: multiple models and perspectives; limited by Folk Theorem, abstraction of mechanisms
  • agent-based: look at wealth vs number of canals; limited by ad-hoc assumptions and params
  • maximum entropy: ; limited by lack of understanding of mechanism

The Evolution of Inter-disciplinary Fields [slides]

Group members:

• high risk, high reward? examples of inter-disciplinary work not doing as well or hurting careers
• instead of looking at success of i-d vs non papers, look at i-d vs non researchers
• by combining APS with arXiv data, can analyze whether prob of or time to publication is different between i-d and non work
• are more authors engaging in i-d work? which fields are more or less i-d over time?
• entropy of subject areas of work as measure of inter-disciplinarity
• lower entropy is correlated with higher eigenvector centrality
• future: study dynamic network of author and/or field connections

Quarantine Strategies in Financial Networks [slides]

Group members:

• banks becoming more centralized, network more connected; too big to fail or too central to fail?
• traditional lit: risk-sharing, robust-yet-fragile, resilience as function of topology
• their approach: containment strategies for shocks (minimize post-shock loss to system), inspired by epidemiology
• simulations: system regulator (recapitalize), self-quarantine (cut edges), and both
• results:
  • given self-quarantine, additional benefit of regulation is small
  • system not fragile to just large banks, but to magnitude of (possibly distributed) shock overall

Topological Modeling of Power Networks [slides]

Group members:

• questions:
  • what are reasonable generative models?
  • can these be generalized to other infrastructure networks?
  • can we infer robustness from structure alone?
• spatial networks, cost of creating new links
• challenge: geo-coordinates of nodes are not typically available (for security reasons), want to infer using weights and distances of links
• use network layout algorithm, multi-dimensional scaling
• existing generative models do not match properties of their dataset (Polish power grid)

Norm Propagation and Reinforcement Learning [slides]

Group members:

• norms constrain behavior, decrease diversity
• people have social identity, formed both by genetics and choice
• change identity based on disposition, which is affected by observing others
• two belief propagation models: ? and Bayesian
• future: impact of external influence/information, cognitive dissonance

A Model of Revolutions [slides]

Group members:

• a revolution is not just a change - it's a phase transition
• model:
  • agents (like or dislike dictator) are the nodes in two networks: government and society
  • choose random node, affects opinion of underlings in gov network
  • interaction society: change opinion based on what your neighbors think
  • in each "round," do gov op, then society op
• studied two parameters
  • proportion of agents activated in society each round (curfew, internet, etc)
  • alpha (material gains, loyalty, religious or ideological identity)
• time until phase shift very sensitive to initial conditions
• questions
  • are revolutions less likely if those higher up in gov are central nodes in society?
  • study dynamics
• Q: what defines a revolution?
• Q: feedback that chaos often shifts public opinion towards wanting a strong leader

Cough Analysis [slides]

Group members:

• pulmonary diseases/conditions categorized into chronic and acute cases
• cystic fibrosis: thick sticky mucus, not like "Triscuits in your nose"
• should be able to detect differences in wave form using ML or other techniques
• online database already running (but has many undesired "features")
• future: develop classification tool, handle other bodily sounds

Co-evolution of Anti-vaccination Sentiment and Flu Infections [slides]

Group members:

• how to measure efficacy; can only measure if person went to the doctor
• risk perception differs by geographical region, shaped by news
• positive correlation between sentiment and vaccination rates
• homophily and contagion favor negative sentiment
• questions:
  • is sentiment correlated with current flu incidence? past?
  • can incidence and sentiment collectively predict coverage?
• from Twitter data: pos/neg sentiment, coverage; lots of technical challenges
• behavioral transitions: "never getting shot again"

Disease Spread in Multiplex Networks [slides]

Group members:

• food webs with parasites, really changes food web structure and dynamics
• multiplex network (spatial multiplex model):
  • trophic transmission layer (food web: eating infected prey/vector)
  • vectorial transmission layer (vectors sucking blood of infected hosts)
• disease spreading dynamics: can spread in either layer or both
• future:
  • investigate role of each species in each environment
  • effect of distance and spatial structure

Fractals and Scaling in Finance: A Comparison of Two Models [slides]

Group members:

• extreme events (fat tails): common modeling assumptions do not match real data
• Levy process
• self-similarity across scales, memory of past, long-term behavior
• "volatility clustering" = burstiness
• fractal Brownian motion allows dependence of processes, but does not have fat tails
• idea: fractal Levy process, composite of two functions
• subordination
• generalized Hurst exponent
• questions:
  • how do fractal Levy process and Subordination compare when it comes to prediction?
  • are there other processes that are good models?
  • what are the economic mechanisms behind fat tails and long-dependence?

Coupling of Networks: Non-linear Effects of Pesticides on Food Web Dynamics [slides]

Group members: Hiroshi Ashikaga, Beth Lusczek, Nhat Nguyen, Sanja Selakovic, Brian Thompson

• model two interacting networks; each has different nodes, different edges, different dynamics which can affect one another
• focus in particular on pesticides and a bio-energetic food web
  • 8 pesticides, 17 species in soil food web
• model:
  • bio-energetic food web: birth rate, death rate, predator-prey, intra-species competition
  • cross-network: effect of pesticide on death rate of each species, non-linear effects of multiple pesticides on each species (can be additive, synergistic, or antagonistic)
  • master equations relating populations of species and concentrations of pesticides
• future work:
  • model consumption, decay, and inflow of pesticides in the system (e.g. degradation of pesticides through consumption by species, bioaccumulation)
  • analyze sensitivity of the food web to changes in pesticide concentrations
• take-away message: simulations and mathematical analysis can help us understand and anticipate the consequences of pesticide use on an ecosystem

Consciousness: An Emergent Phenomenon? [slides]

Group members:

• agreed on:
  • consciousness is a unified subjective experience
  • project should be limited to human consciousness
• question: can we perceive the emergence of consciousness in an artificial agent?
• integrated information theory: anything that can perceive, process/integrate, and emit information is conscious
• their model
  • hierarchy: neurons, nervous system, brain structures, brain, organism, environment
  • integration of info, but also hierarchical anatomical level, emergence from interactions
• can we apply AI to simulate conscious agent?
  • Minsky: don't try to capture everything in one word, break down into multiple parts (suggests 20)
  • focus on: self-reflection, decision, reaction (fits model of AI brain)
  • consciousness and HCI: they believe that all 20 functions can be simulated, interaction is essential
  • can humans recognize X in the computer? maybe not, so instead suppose we have an oracle that can
• future: AI experiments can allow us to simulate these functions, which the integrated info theory approach can't do

Persistence of Pollination Systems [slides]

Group members:

• many pollination systems rely heavily on synergistic relationship
• nenctar robbers eat pollen but don't help fertilize
  • but could have indirect positive effects
• model 4 species: open flower, tubular flower, pollinator, robber
  • ODE model
  • coexistence when pollinator does better than robber on open flowers but less on tubular, and ? > 1
  • abundance over time is chaotic? with linear center
• agent-based model
  • analytical model must assume good spatial mixing, but spatial dynamics are essential for pollination analysis
  • model movement of bees, energy intake and consumption, reproduction
  • flowers reproduce only if pollinated by same species (open/tubular)
• results:
  • if more pollinators than robbers, then pollinators and open flowers survive
  • if more robbers than pollinators, then everybody dies
  • space and stochasticity can have an effect
• future:
  • sensitivity analysis
  • find parameters to make ABM match analytical model
  • allow for mixed or dynamic strategies of bees
  • out-crossing

Bitcoin [slides]

Group members:

• focused on three major events (volatility is interesting)
• look at network, highly connected core, long chains thought to be money launderers trying to mask trail
• measures of complexity, order, and freedom (MacArthur and U...?)
• real systematic change in structure of network during different periods captured by various measures
• network properties -> ? -> bitcoin dynamics; what is the "?"
  • sentiment analysis of newspaper articles, blogs, etc.
  • observed differences in sentiment around key dates

Social Institutions and Economic Inequality [slides]

Group members:

• model relationship between economic inequality and economic growth
  • Kuznets curve (wealth balanced, then inequality increases, then stabilizes back to equality)
  • ?: inequality induces democracy
• existing analytical model is flawed for several reasons
• their (agent-based) model
  • question: how does inter-class marriage affect timing or shape of Kuznets curve
  • model via assortativity coefficient
• results: lower assortativity yields a Kuznets curve that is shorter and earlier
• future:
  • sensitivity analysis
  • model collective action
  • look at empirical data
  • how can we inform policy with this kind of model?

Drunk Game Theory [slides]

Group members:

• perceived payoff changes as function of individual state
• model
  • cooperate = buy beer for oneself and partner
  • defect wait for other
  • payoff = when sober, net gain of beers (free - paid-for); when drunk, net intake
  • parameters: coefficient of intoxication, sobriety constant (per person)
• assumptions: uniform distribution in bar; ?
• analytical study: vector fields, fixed points:
• simulations: fixed point reached depends on initial conditions and parameter values; matches analytical predictions
• summary
  • new branch of game theory and mathematics!
  • in evolutionary game theory (EGT), players change; in drunk game theory (DGT), the game changes
• future: genetic predisposition, pre-drinking behaviors, segregation vs socialization