Complex Systems Summer School 2014-Project Presentations: Difference between revisions
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==Presentation Notes== | |||
===Towards a Unified Theory of Biodiversity [[Media:Presentation_Biodiversity.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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? [[Media:Presentation_CellsAndSoftware.pdf|[slides]]]=== | |||
Group members: <br> | |||
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 [[Media:Presentation_InfoTheoryOfTheHeart.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_DivisionOfLabor.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_GenotypeNetwork.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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? [[Media:Presentation_AntsAndCities.pdf|[slides]]]=== | |||
Group members: <br> | |||
Claire, Diana, Morgan, Bernardo, Michael, Alex, Ernest, Alberto, James and Rohan <br> | |||
* 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 [[Media:Presentation_RobustnessInNetworks.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_Cameroon.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_InterdisciplinaryResearch.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_QuarantineStrategiesInFinancialNetworks.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_TopologyOfPowerNetworks.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_NormPropagation.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_Revolutions.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_CoughAnalysis.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_AntivaccinationSentiment.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_MultiplexNetworks.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_FractalsAndScalingInFinance.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_CouplingOfNetworks.pdf|[slides]]]=== | |||
Group members: Hiroshi Ashikaga, Beth Lusczek, Nhat Nguyen, Sanja Selakovic, Brian Thompson<br> | |||
* 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? [[Media:Presentation_Consciousness.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_Pollination.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_Bitcoin.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_EconomicInequality.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 [[Media:Presentation_DGT.pdf|[slides]]]=== | |||
Group members: <br> | |||
* 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 |
Latest revision as of 01:08, 4 August 2014
Complex Systems Summer School 2014 |
Use this space to post project presentations and outlines. Include group members, a brief outline, and your slides.
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:
Claire, Diana, Morgan, Bernardo, Michael, Alex, Ernest, Alberto, James and Rohan
- 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)
- simple foraging model can produce properties common to both city and ant networks
- 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