Computer Science in Science PD: Agent Based Modeling of Complex Adaptive Systems - Discussion


The concept would be energy transfer within a system: Why do things stop moving
Agents: different types of energy ( Kinetic Energy, Thermal Energy, Gravitational Potential energy, elastic potential Energy…)
Environment: any system: Pendulum, basketball, Swing

Students will be able to manipulate different types of energy present in a system and understand the effect on the system and how fast or slow a moving object will come to a stop.


I think I focused my last answer too narrowly: I focused on the albedo effect because I was thinking perhaps too much about the feedback loop element. So let’s just broaden to the Arctic environment. Although agent wasn’t specifically defined in the little video, it seemed to be based on living organisms. So my agents would be species that live in the Arctic: some bird species, Arctic hare, fox, caribou, polar bear, walrus, lichen. Environment: I would need to input data about incoming radiation, amount of snow and ice cover, amount of water, temperature. And I would need data about energy requirements for each organism, food options, conditions under which they obtain that food. Personally, it seems like reality is way too complex to accurately input into what seems a simplistic and very narrow model. So I am not at this point convinced of how this is going to be genuinely useful. The one she described seemed way too simplistic too.


That sounds really interesting. I’ve been reading lately that the replacement of cutthroat trout by nonnative species is actually having a widespread impact on the ecosystem also and I wonder if that could be modeled as well.


In my previous reply I indicated that the phenomenon to model would be the evacuation of Atlanta, aka The Walking Dead.
The agents would be: zombies, uninfected humans, infected humans who have not yet turned
The environment would be the streets of Atlanta
Interactions would be: Zombie/human encounter each other. Zombie bites human and human either turns or is killed by self or other human before turning. Human escapes w/o getting bitten. Zombie killed by human.


I would use it with the introduction of an invasive species in the Great Lakes. The agents would be the zebra mussels, plankton, and other fish in the ecosystem. The environment is the Great Lakes.


I would like to model it with the first generation game called Plague, the rated “E” Everyone version. Its a game that really is a simulation of how successful is the global transmission of a disease. You get to name the disease, the starting point on the globe, at what stage is the disease contagious. The agents are humans, the environment is the world. The interaction is contact with other humans and the spread via simulated world travel.


Concept- Plate tectonics
Agents- movement of the plates in the lithosphere, magma, Earth’s crust, humans
Environment- the area above the movement of the plates
Interactions- movement of the plates and its affects of magma, magma movement and its affects on the crust, crust movement and affects on humans


The concept I thought about was invasive species. I use to teach an entire unit about how invasive species effect the native species of Illinois. The agents would be the invasive species such as the Asian Big Head Carp and other aquatic life found in a lake. The environment would be Lake Michigan. The interactions would be to show how the increase of the invasive species in an area could effect the population size of other species nearby.


In Chicago, a group of cat people started a program where you can “adopt” a feral cat (they stay outside) and that cuts down on the rat problem in people’s backyard. The agents would be the cats and rats and perhaps even the people since they feed the cat too. They would interact with the urban backyard environment.


That’s another good Chicago based problem.


My idea focused on natural selection, predator-prey, and environmental variables. The agents would be mice (dark & light-colored coats) light sandy environment and dark forest floor environment and coyotes. The program would run to find out the outcome in population of prey when their fur is easily camouflaged with the environment as opposed to in contrast with the environment.


The complex adaptive system I chose to identify earlier was: Wolf vs Elk: Predator/Prey relationships.


  • wolf
  • elk
  • grass, plants, leaves, etc.


  • forest

Interaction between agents and/or between agents and the environment:

  • wolf consume deer
  • deer consume grass, plants, leaves, etc.


The system will be the interaction of lake algae, fish and aquponics. The fish excrete waste after eating plants. Waste is absorbed in water and sent to aquponics tubes to feed outside plants. Algae adds a oxygen and nutrients for both plants, fish and external sources.


We do wolf, plants and Rabbitts every year in our text. Very complex system to be displayed, but I am excited to use the CAD.


The system would be the producers/consumers in an area. The agents are the populations of producers and their diversity, the populations of consumers and their diversity, as well as the environmental conditions. The availability of resources would affect the interactions between agents and the environment.


A take on that would be the motion of the objects “picked up” by a tornado.


I identified humans and their activities and how this adds to climate change.
The agents would be humans and an activity such as using aerosol spray. The environment would be a neighborhood or region on Earth and the interactions would be when human partake in the activity.


Example of research using agent-based modeling methodology to investigate individual and
social factors underlying inequitable participation patterns observed in a real classroom (environment) in
which an experimental collaborative activity. Researchers created agent-based
simulations of simplified collaborative activities and qualitatively compared results from
running the model with the classroom data. They found that collaboration pedagogy
emphasizing group performance may forsake individual learning, due to preference for short term
group efficacy over individual long-term learning.
A classroom engaged in collaborative group work can be seen as a complex adaptive system (Hurford,
2004) in which optimal as well as sub optimal behavioral patterns may emerge. Despite individual students’ initially exploratory behaviors, once a functioning coordination scheme evolves in a group and is evaluated as
well adapted to performing the mandated task, an implicit quietus is set on any further exploration or task
rotation, and the group achieves dynamic stability. Such arrangement would be fitting for workplaces, but its instantiation in classrooms may present teachers with the dilemma of maximizing group production at the expense of individual learning, especially of struggling students who are benignly assigned by the group to mathematically lesser tasks. ABM methodology may provide education researchers and practitioners tools for understanding such classroom dynamics, so that they can identify points of leverage for working with students’ natural behavioral inclinations to achieve equitable participation.
we chose a simpler numeric puzzle task (see Figure 1a).
This linear puzzle consists of set of numbered pieces to be concatenated in ascending order (1, 2, 3, 4…).
Necessary activities within this task are retrieving pieces (simplest task), verifying if pieces are already present
in the puzzle (intermediate demand), and connecting pieces (most demanding task). Initially, puzzle pieces are
scattered all over the classroom. Piece-retrievers wander around and, when they find a piece, grab it and go back
to their group’s table, delivering it to the piece-verifier. The piece-verifier evaluates whether a copy of the piece
is already present (the puzzle cannot have repeated pieces). If so, the piece-verifier orders the piece-retriever to
discard the piece and bring a new one. If the piece is suitable, the piece-verifier delivers it to the piece connector, who will check if the piece fits the puzzle in its current state, and connect it to the puzzle. For each successful micro-task, students receive positive feedback in the form of an increment in their skill (speed and/or accuracy). Overall group performance is evaluated by the time-to-completion divided by the number of correct pieces. Our independent variables are: (a) pedagogical style (with or without mandated role rotation); (b)students’ initial skill level for each task and distribution of skill levels within students; © task difficulty.

after many sets of experiments over a large initial parameters set, we were able to plausibly demonstrate
relations between pedagogical practice and student learning, as follows: (i) The overall performance of groups
with mandated role-rotation decreased by approximately 40%; (ii) When student–agents were reinforced for
group performance rather than individual learning, students became entrenched within skills reflecting their initial skill-level distribution; However, when role rotation was mandated, even though production slowed down, more learning occurred, per student.
Increasing a low-level task skill (i.e.,increasing the number of puzzle pieces a retriever–student can bring to the group per time tick) appears to decrease the correct/incorrect puzzle-pieces ratio (failure ratio) linearly,whereas increasing the high-level task skill effects a non-linear trend .


Climate change:
Agents- humans
Activity - pollution
Environment- interactions when humans partake in releasing into the atmosphere


I chose the immune system. The agents are the specialized body cells, the environment is the gut, and the interactions are the specialized body cell with harmful foreign material.