Gamification & Agent Based Modelling (ABM)

Gamification is a method of social research that is becoming increasingly widespread. As the name suggests, it is an attempt to pack certain phenomena into a game in such a way that they become accessible to social science research.

In principle, social science research is subject to a certain micro-macro problem. While the object of the social sciences is the social, in contrast to psychology, for example, which focuses mainly on the individual psyche, the social is not accessible to direct investigation. Sociology essentially makes do by making individuals the subject of its investigation and gaining an insight into the social component of each individual by generating sufficient data, or it categorises macro-phenomena and orders by proceeding historically-analytically. Both approaches proceed qualitatively as well as quantitatively, but have the equal difficulty that the social dimension lies hidden behind their data, so to speak, and must first be unearthed through social science work.

Gamification offers a way out of this difficulty. Groups of people playing together appear in these games not only as a group of individuals, but also as a unit, each with specific characteristic properties. Conversely, however, they are also in a sense separated from society as a whole by the controlled environment of being embedded in the game as players. The social macro-phenomena can reveal themselves in the game behaviour, but they do not explicitly determine the game behaviour. The social role that a player plays “in the real world” does not have to be fully expressed in his or her gaming behaviour. Conversely, however, the social footprint is expressed in the game as game behaviour, i.e. epistemological basic ideas, values, aspects of the collective psyche and also the social order.

This makes it possible to use games as a laboratory for social research. The controlled environment of a game allows for an explicit demarcation from the social environment, but at the same time it allows for research into the group of players as a social entity.

Gamedesign in AI NAVI

This makes the purposeful development of games an important methodological procedure in social research. Research in the social laboratory “game” must be suitable for actually producing those phenomena that are to be researched.

Already within the planning grant of AI NAVI, two games in particular have been developed that should allow to shed more light on research questions within the framework of AI NAVI: the Party Game and the Corona Game.

The party game is relatively simple and primarily designed for the co-creative aspect by the players. The foundation is simple: players find themselves at a party where they randomly end up at different tables. Based on a rule, they either feel comfortable at that table and want to stay there or they don’t feel comfortable and want to leave the table. The rules for feeling comfortable have been pre-determined by the players and are meant to have a gradient of difficulty. For example, the initial rule that the number of table neighbours of the opposite sex determines well-being has been supplemented by the players with a rule stating that anyone standing next to a person with white socks feels uncomfortable, as well as a rule stating that dice are rolled to determine who feels uncomfortable. A total of six rules were designed by the players, which became increasingly complicated and displayed more complex behaviour of the group of players.

Before and after the game, the players were asked to rate how “complex” they found the game, and additionally to rate their “satisfaction” with the game after the game. The aim of the game was to let the players explore for themselves, so to speak, how the complexity of the gameplay related to their satisfaction. In addition, a marginal aspect was that the players were not asked about their assessment of the difficulty or complexity of the game, but in particular about their assessment of the complexity of the game. This gave the players an insight into their specific understanding of how complex they felt the game rounds to different rules.

It is precisely this second aspect that reveals a strength of the gamification approach: the empirical calibration of descriptions. In contrast to the approach of predefining terms lexically, players’ assessments of a game are asked for and compared with the data obtained from the games.

Agent Based Modelling

This aspect can be deepened even further by additionally reenacting the game action digitally. In fact, the party game originates from the so-called party simulation, one of the most elementary ABMs.

ABM is a form of computer simulation that represents a further development of the concept of cellular automata. ABMs consist of agents that interact with each other on the basis of predefined rules. In the process, patterns develop that make it possible to investigate the emergence of phenomena in the interaction of the agents. A natural field of application for such ABMs is therefore, for example, innovation networks, as in the SKIN model, where conclusions can be drawn from the interaction of stakeholders within such innovation processes as to the conditions under which innovations are successful.

At the same time, ABMs also allow an interaction to be analysed algorithmically. For example, the rules of the party game were implemented in an ABM and at the same time analysed in terms of measures of algorithmic complexity and these numbers were linked to the players’ assessments. This resulted in correlations between algorithmic complexity metrics and the players’ assessments. This allows for a more vivid understanding of the relatively vague notion of complexity that emerges from the players’ survey.

The sweet-spot and more complex games

The actual aim of the party game was to identify the sweet-spot phenomenon, i.e. whether there is a comfortable level of complexity. The original hypothesis was that very little complexity is boring and unexciting for players, so they tend to increase the complexity they are exposed to. At a certain point, however, the complexity becomes overwhelming and players begin to feel uncomfortable with the complexity they are exposed to.

This hypothesis was confirmed in an interesting way in the first exploratory trials of the game. The assumed U-shape of the complexity-satisfaction curve did not appear in the data. Rather, the data rudimentarily revealed an inverted “W”. While the basic assumption that both too little and too much complexity was unattractive to players, there was an additional slight drop in satisfaction near medium complexity. Even being exposed to “normal” complexity is not necessarily a particularly satisfying state for players.

This first game, however, served primarily to test the basic approach of combining gamification and social simulations. The game is still too simple for a closer exploration of complex dynamics in social interaction. In particular, the difficulty in distinguishing complexity from mere intricacy may have entered the data as an artefact of player assessment. This makes it necessary to develop the approach further. There are three main directions in which the gamification approach has already begun to evolve.
1. more complex games
2. the use of AI to analyse algorithmic complexity
3. learning agent based models

The corona game

As part of the planning grant, another game has already been designed and tried out for the first time: the Corona Game. The Corona Game is a game in which a community of players must maintain their daily lives. The players have a number of options for action: they can work or go to school, they can buy products in the supermarket, do banking business, decide on measures in the town hall or spend their time in a lounge. However, there is a pandemic in the game community and the corona virus is going around. The players must therefore master a pandemic situation with their game behaviour, but at the same time everyday necessities such as earning money or running errands must still be taken into account.

The game is used to examine patterns of cooperation in a tense situation. Thus, the players can decide on socio-economic assistance such as minimum wages, health insurance, vaccination development funds or a social welfare system. At the same time, they can also earn extra money and protect themselves by buying shares and products such as disinfectants.

The game is based on a comparative cultural approach, which makes it possible to analyse the game in terms of culturally typical approaches to cooperation. By combining the game with another game, the value auction, in which the players can bid for values, it is possible to examine game behaviour with value concepts and the establishment of collective strategies for coping with crises. The first test games have already shown correlations between values and game strategies.


The corona game and the party game differ fundamentally in the way complexity occurs in them. The corona game is open and explores the formation of complex cooperation patterns without a clear end state. The party game, on the other hand, has a clear end state that all players at their table are satisfied and explores the complex patterns that emerge along the way.

Especially the latter allows the use of and analysis by means of AI algorithms. Already in the first analyses, neural networks were trained to understand the rules of the party simulation. The learning curve of the neural network thus allows conclusions to be drawn about the learning behaviour of human players and thus provides initial insights into the cognitive challenges of dealing with a simple phenomenon: players move or do not move. The AI has to find out why they move or don’t move in order to be able to judge the game.

Another approach in which AI systems and ABM are coupled in AI NAVI are so-called Learning Agents Based Modelling (LABM) systems, in which the rule-based interactions between the agents are replaced by interactions based on reinforcement learning. These LABM are particularly interesting in the case of games with a competitive aspect: the individual agents have clearly defined actions and a clear goal. It remains unclear how they achieve this goal and not all agents can achieve their goal at the same time. The concrete behaviour then results from trial-and-error and reinforcement learning heuristics. The agents try out behaviour and learn from it which behaviours are more promising than others. The behaviour learned by the other agents up to a certain point also serves as a basis for the further development of each agent’s game strategy. This makes it possible to more clearly grasp and analyse the evolution of complex game strategies that also arise in games with humans, as well as to investigate tipping-point phenomena in which a specific and more or less random situation is responsible for the development of a more fundamental subsequent situation.