I'm putting together a list of ongoing modeling works (from different research areas) which try to understand and predict the behavior of an interesting phenomenon. Just a few examples to kick off the discussion:
- In environmental systems, global climate change modeling seems to be a perpetual challenge. Making climate change models accurate enough for quantitative prediction has been bedeviling such models.
- In obesity and nutrition, the current literature provides over 100 statistical equations to estimate basal metabolic rate (BMR) as a function of different attributes (e.g. age, weight, height, etc.), yet understanding how BMR is precisely modeled based on those attributes is an interesting area of research.
There seem to be tons of examples in biology, psychology, economics, engineering, etc, but what are the 'publicly interesting' challenges that you would like to add here?
I am currently working on strategy issues. It is really hard because the phenomena are quite elusive and the problem boundaries must be thoroughly stated or you come up with a messy useless model.
An MD, working with my supervisor, specialized in the area of sports and physical rehabilitation is currently using SD to model hypoxia issues for high performance athletes for his PhD thesis. In medicine there are plenty of areas that would benefit from this approach because the nature of the phenomena is quite hard to analyze.
The network science field presents new and interesting modelling challenges.
In his paper "Catching the Network Science Bug: Insight and
Opportunity for the Operations Researcher" David Alderson identifies three questions he believes should be included among the most interesting ones to researchers in network science nowadays. Quoting:
"
• Does there exist a network structure that is responsible
for large-scale properties in complex systems? Typically,
the properties of interest range from traditional engineering
concepts such as performance and reliability, to opaque
notions such as flexibility, adaptability, and sustainability.
• Are there universal laws governing the structure and
behavior of complex networks? In particular, to what extent
is self-organization (i.e., coordination from the “bottom
up”) responsible for the emergence of system features not
explained from a reductionist (i.e., “top down”) viewpoint?
• How can one assess the vulnerabilities or fragilities
inherent in complex networks to avoid “rare, yet catastrophic”
disasters (e.g., the August 14, 2003 power outage
in the northeastern United States)? More practically, how
should one design, organize, build, and manage complex
networks?
"
You can find the paper in here:
http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA487138
Capital jury sentencing reccomendations -- these are life/death decisions made in secrecy
@Fernando Pimentel
You may be interested in this.
"Early-warning signals of topological collapse in interbank networks"
http://www.nature.com/srep/2013/131128/srep03357/full/srep03357.html
This is a graph-based modelling of the inter-banking structures that shows early signs of bank failure.
In the context of environmental science it is of relevance to understand human behavior on multiple scales. Why do people act against better knowledge? When and why do people cooperate to share resources? Which organisations are adaptive enough to respond in time to gradual change or external shocks?
Challenging in this kind of models is that people constantly develop new tools and behavior. It is hard to say whether we are doomed by an ever growing population or whether we have enough head/heart skills to find a sustainable way of living.
So, what are we goint to do about it?
@Augusto thank you for the link to the paper which I found quite interesting.
I'd think modeling neurological processes at the neuron-synapse involving a "realistically" sized portion of the human brain would be one of the most challenging and scientifically fruitful simulation exercises. By this I mean creating morphologically accurate repressentations of an astronomical number of neurons, along with their dendrites, axons, spines ans synapses, connecting them in realistic "neural networks", and simulating impulse transmission and the progressive strengthening or weakening of synaptic transmission in response to environmental inputs. Doubtless CERN's Blue Brian project has taken significant steps towards such realistic brain modeling.
I think connecting quantum-biology, microbiology and cell biology is super interesting, but the varying scales make it very difficult. Think about it: the generation of energy through photosynthesis is a quantum process, dependent on photons striking and travelling through the reaction centers. However, it is affected by the availability of ATP or NADH for regeneration, which is in turn affected by the overall state of the cell. So many proteins, each governed by quantum effects, acting together to keep a cell alive...
Does anyone have resources or contacts that would,enable me to obtain employment working on the exciting projects we've discussed? It's all very well to talk about the Human Brain Project, etc. It would be far more satisfying to work on them!
There are always interesting and challenging phenomena to Melodee out there. However, it depend on what questions were are interested to answer. In biology, for, example, seed dormancy is a complex system to model, in my experience of course!
We will research how system dynamics modeling can be applied to enable the balanced scorecard to fulfill its potential as a strategic management tool. We will develop a simplified model of an organization in the context of the balanced scorecard. The original balanced scorecard consists of four fundamental sectors; in our model, we include a fifth sector, called the environment, to represent the ecological aspects of sustainability. (s. http://kutzschenbach.eu/zur-person-2/activites/)
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