An argument that interests me is the debate concerning what value simulations provide for researching new ideas and garnering new knowledge. As Herbert Simon succinctly puts the argument in The Sciences of the Artificial, “How can a simulation ever tell us anything that we do not already know?” The arguments backing this skeptic’s question are that 1) simulations are no better than the assumptions they are built on and that 2) simulations can only do what they’ve been programmed to do.
Simon continues with a response which is far more concise and sound than I could express in my own words…
There are two related ways in which simulation can provide new knowledge. … Even when we have correct premises, it may be very difficult to discover what they imply. … [We] must tease out the consequences of our assumptions. … [For example] attempts have been under way for some years to apply [simulation to weather and weather prediction. While simulations for this environment are] greatly oversimplified, the idea is that we already know the correct basic assumptions, the local atmospheric equations, but we need the computer to work out the implications of the interactions of vast numbers of variables starting from complicated initial conditions.
Accordingly, simulations provide a means to create “canned” environments, or scenarios with known initial conditions, and to watch the implications of our assumptions play out on those conditions. Obviously, simulations are also immensely assistive as a means to test out new ideas without investing large amounts of time and money into speculative hardware requirements.
Certainly a challenge with the creation of any simulation for the purposes of knowledge creation is 1) the realistic re-creation of the environment and 2) the realistic re-creation of the agent which will be responding to stimuli and/or acting upon the environment. Indeed, one can find him/herself spending as much time on generating the environment and simulated agent as they might in actually building the agent itself (e.g., a mobile robot). Some vendors have taken great strides in recognizing this challenge and accommodating users, accordingly. For example, while simplistic, Lego NXT robots may be designed within Lego Digital Designer and converted into Robot Operating System (ROS) compatible models using the NXT-ROS stack to rapidly produce and simulate an assortment of ideas in differing environments. Increasingly available tools and solutions, such as this, are allowing vastly more complicated scenarios to be brought into varying, simulated environments for feasibility testing of new ideas. (If you’d like to discover many other available simulation environments, I invite you to also take a look at Simulations Environments for Mobile Robotics.)
So while it’s difficult to replace the feedback and learning of “the real-world,” simulations still provide a veritable proving ground for observing the implications that well-grounded assumptions, with known initial conditions, have in a wide array of possible environments, with sometimes surprisingly real-world results.