The game let's you play an iterated prisoner's dilemma. Do you know the prisoners' dilemma? Not sure about the payoffs? Have a look at Nicky Case's model for an easy to remember ruleset:
You have one choice. In front of you is a machine: if you put a coin in the machine, the other player gets three coins – and vice versa. You both can either choose to COOPERATE (put in coin), or CHEAT (don't put in coin).
Experience game theory with entertaining graphics by Nicky Case
Now visit the game page of the game of trust and have fun!
Today I was giving a lecture to kids at age 8 to 12 at our University. The lecture was part of an initiative called “Kinderuni” (Children’s University) which aims at increasing awareness of our academic business already at young age.
In my lecture I approached the general topic of computer software by the example of the programming language Scratch. Scratch is a graphical programming language designed by the Lifelong Kindergarten Group at the MIT media lab. The language aims to be simple, colorful and fun in order to enable and motivate children at a young age to create programs with their own ideas.
I explained how Scratch works and together the kids and I coded a simple computer game in 25 minutes, which was definitely a challenge to do this in this short time. Another challenge was to create the connection between making a simple game with scratch and doing research at a university. However, this might be easier than you think. While Scratch is in general a programming language for kids, it can be actually useful to explore and demonstrate multi-agent behavior with comparably little effort. Especially with the introduction of cloned objects in Scratch 2.0, the implementation of swarm behavior with a variable number of interacting agents became easy.
Some projects from swarm behavior studio
The Scratch Studio Swarm Behavior gathers online simulations and games related to swarm behavior, multi-agent systems, clone interactions, self-organizing systems, and artificial life. The simulations show how Scratch can be used to demonstrate swarm behavior and how such a simulation can be implemented. Scratch is of course of low value regarding functionalty and performance - so you might have to drop the idea of having kindergarten kids coding the simulations for your next journal paper ;-).
One of the properties of self-organizing systems is scalability. It means that system keeps its working capabilities even if we remove some of its components or add more of them. In our reseach, we employ different evolutionary algorithms (EAs) to create a self-organizing system. In particular, algorithms like a simple evolutionary algorithm or a two dimensional cellular EA are used for adjusting the synaptic weights of an neural controller. The best solutions are identified based on simulations of the target application. Typically, the simulation parameters limit the applicability of the solution - there is no guarantee that an evolved solution is adaptable or scalable to situations not specified in the simulation parameters. On the other hand, there are many examples in nature where solutions could be successfully employed in other contexts. We decided to check how our soccer teams, which consist of evolved neural controllers, can scale.
For the FIFA World Cup in Brazil we organized our own tournament between evolved self-organized soccer teams. This is an exciting show - to see how simple agents having only partial information about the environment around them are reaching its goal (score a goal) as a team. Will they be able to play in the same manner if we take the contoller, trained in the simulation with 10 players per team, and increase or decrease the number of players? This question has remained open until today.
In our first scenario, we assume that we invited two soccer teams to show us a fantastic game, but due to some circumstances, only 4 players per teamshow up.
Thus our first experiment can be seen in the video below.
Despite the players being evolved in a context of 11 players on each side, reducing the number of players did not affect the ability of players to show good game.
To check the other extreme, we settled a very dangerous experiment - each team consisting of 40 players! The results were stunning (see video below). These soccer heroes could play as a team even with significantly increased number of players. Unfortunately, they could not play for a long time in this mode: Marco Materazzi headbutted Zinadine Zidane in the chest and shouted "Revenge!"; Luis Suarez bit two players in order to show his perfect teeth; Diego Maradona scored the goal by striking the ball with his hand and this time he was disqualified for this trick. We didn't care about these incidents since we got the results of our experiment:
You cannot wait for the soccer World Cup to start? We proudly present a peek preview of the World Cup 2014 - played by teams created with evolutionary algorithms. Using our evolutionary tool FREVO for designing self-organizing systems we have evolved neural networks that make robots playing soccer. During the evolution phase, a fitness function combines different aspects of gameplay like zone defense, man-marking, passing, shots, and goals. By tweaking the weights for these parameters we can influence the playing style of a team while the overall gameplay is still generated automatically by the evolutionary process. Thus we can simulate playing styles of different national teams and then match them against each other.
The following video shows a simulation of Brazil versus Croatia, the opening game of the world cup. The commentary is from Toni Polster, a legendary Austrian soccer player.
While the result is credible, we have not done this to exactly predict the outcome of the games - this would spoil the whole tournament! Furthermore, our approach is not meant for prediction but a system to train a distributed agent-based system to achieve an emergent cooperative behavior in a self-organized way. Setting up this work helped us in improving our understanding how we can create and guide self-organizing systems. We have chosen the soccer simulation as a demonstration because in soccer the global goal (no pun intended) can be achieved in so many different ways , for example with a defensive, offensive, kick-and-rush, pass-intensive, etc. style. And it is nice to watch - who said good science can't be fun!
Maxis has quite a record in providing interesting simulation games since they came out with SimCity. The game SimLife: The Genetic Playground, however, never became a hit. In the game you can simulate an ecosystem including a climate simulation, a plant groth model and a complex model of animals including herbivores (plant eaters), carnivores (meat eaters), and filter feeders. Not enough, they added an evolution model including genes and phenotypes for all plants and animals.
At this point it becomes clear why this game was without success: it is more a research simulation than an actual game. The number of statistics and graphs also support this impression. Moreover, I guess that the actual processing power at the beginning of the nineties did not allow for extensive simulation experiments. Another distinctive feature between a game and a scientific experiment: a game is designed to give the player a fair chance of success (at least in the lower level). In contrast, SimLife simulations tend to end up in extinct animals and low-diversity flora very often. For example, Spore (from Electronic Arts, which bought Maxis some time ago) has a similar scenario, but is designed as a game. I was not really able to create a stable ecology with more than 5 different species, but still I prefer SimLife over Spore.
In overall, SimLife is interesting from a complex systems point of view even still today. If you want to try it, find it at some abandonware site and run it using an emulator, e.g. dosbox.
