The science behind Alien CODE
Most of the games on ScienceAtHome are a search in a complex landscape, where we look for the best solution to a given problem. This search can be conceptualized as walking through a high-dimensional complex landscape with some rationale behind what the next step should be, a process called optimization. The most efficient optimization method is very dependent on the problem type and it is not always straightforward to identify the proper method. E.g. in quantum physics it had been shown that all peaks in the problem landscape had equal height given there were no restrictions on the properties of the solution . Thus, a simple local optimization, which finds the top of the first available peak, would do just fine. Quantum Moves showed, however, that as you introduce some limitations on the solution such as how slow it is allowed to be this assumption breaks down, and other methods are required .
There is a long tradition of social science looking into human problem solving as an optimization problem, i.e. people trying to improve their performance by paying attention to what they've done in the past, much like some computer algorithms. Yet, humans seem so much better at it (Carruthers and Stege 2013). Research of this kind has so far been purely conceptual, using optimization search as a model of how humans solve problems. Human problem solving is such a tremendously complex cognitive endeavor that even though there are strong traditions across multiple fields (psychology, cognitive science, anthropology), this research has brought little insight into how humans solve problems. However, by creating an environment where people solve highly complex optimization problems (wrapped inside a game) we can more thoroughly understand human problem-solving behavior, by i.e. benchmarking it to computer algorithms.
Answering this question might help us to design better algorithms to solve very complicated problems like how to design a quantum computer.
Figure from Nature
In Crystal Crop Fever the landscapes are well understood and designed to test how people work together when they are confronted with an unfamiliar and complex problem. Thus, we will vary on some of the underlying conditions .
You will notice that the Alien CODE looks quite different than Crystal Crop Fever (CCF) or Alice Challenge (AC). However, as explained above, fundamentally, we are interested in the same problem: how do people go about searching for solutions in unknown environments. In the AG, the landscape has several structural properties that make it suitable for the investigation of complex human problem-solving.
In the Alice Challenge we posed a unique question: if the CCF and AG landscapes are chosen by the researchers, one may argue that the observed problem-solving behaviours are not ‘general problem-solving behaviours but are somewhat endogenous to the task. By choosing how the task looks like we elicit a particular type of behavior, but do people actually behave in a similar way (can we find similar patterns) when dealing with a real-life problem.
By studying problem-solving across all these different contexts, the hope is we can identify general human problem-solving patterns that can form the basis our designing of artificial agents that can help us in solving a wide range of natural science challenges.