Policy and Complex Systems
( National Alliance for Caregiving , 2009 ). To test this policy option , Ihara , Horio , and Tompkins ( 2012 ) used an ABM to explore the likelihood that grandchildren would become a primary caregiver for a frail grandparent . They found that a targeted-policy scenario where high-income families do not get a tax credit , middle-income families receive a $ 3,000 tax credit , and low-income families receive a higher tax credit had better results for motivating grandchildren to become caregivers than the universal policy of a flat tax credit for all caregivers .
These various policy options potentially are the foundation for the decision-making process of an older adult and his / her family regarding the best living situation including independent living , home-based supportive living , assisted living , or nursing home placement . Further , these options may not necessarily alleviate the burden for all families , pointing to the need to better understand what mix of services and support can enhance the decision for caregivers and care recipients . Unlike mathematical models of a society , which represent all or large portions of a society as a single unit , ABMs represent the individuals and emerge their collective behavior . This leads to the research question of how to forecast caregiver stress for those providing support to individuals with dementia . We took an agent-based modeling approach and explain the details on how the model was built , run , and analyzed .
Methods
Agent-Based Modeling
Agent-based modeling is a computerbased simulation methodology that can support testing of policy options . The idea is to formalize processes in a computer program , which can then be run with different policies implemented and the simulation can report the effects . Such a simulation includes models , that is , computational representations , of the conditions and processes people live in ( their environment ), the people themselves ( called agents ), and how they interact . This is not simply having the computer calculate the overall effects through mathematical formulae for the behavior of the overall system for different initial conditions . The key idea in agent-based modeling is that the modeling is at the level of individual agents who sense the environment and respond reasonably . Agent-based models ( ABMs ) can represent the diversity in a human society and then produce individual and appropriately varied behavior in their responses to changes in the environment .
While modeling cannot provide an exact fit to reality , Epstein ( 2008 ) discussed 16 reasons , or benefits , for modeling . For the purposes of this paper , the salient reasons for addressing this topic by modeling include :
1 . the tendency of humans to create implicit models in our minds with ABMs being explicit and calibrated to the actual data ;
2 . modeling assumptions are laid out in detail so that changes can be observed when the assumptions are altered ; 3 . sensitivity analysis can be conducted ; 4 . models can lead to new questions ; and 5 . modeling enforces a “ scientific habit of mind .”
Simulated Model
To focus on the decision making involved with this topic , we use a mixed approach . Overall , this is an ABM ( Gilbert , 2008 ) with the individual agents built on simple system dynamics models of their health and stressors . The model is implemented in NetLogo ( Wilensky , 1999 )
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