Warning: This game is still in active development. Your experience may change as I add new functionality and tweak features to enhance realism. I will strive to keep these instructions updated as the game evolves.
You control the backlog and running experiments for a small development team. Each day you make decisions that impact product development and overall sales.
Your current progress is displayed at the top of the screen. Once you are done for the day, click to move the simulation forward.
After {{settings.game_duration}} days, the game is over and your final score is computed based on total sales.
Your objective is to maximise total sales.
Backlog
Your teams maintains a simplified backlog. Different tasks can be prioritised or deleted entirely. If nothing is prioritised, your team will pick up a random task when needed.
Each task has visible and invisible properties, depending on the feedback level.
Topics
Each task is related to one of 5 different topics:
{{backlog_topics[0]}},
{{backlog_topics[1]}},
{{backlog_topics[2]}},
{{backlog_topics[3]}}, or
{{backlog_topics[4]}}.
Different topics share different underlying probabilities of success and effect sizes.
This means that all tasks of topic {{backlog_topics[0]}} will tend to show similar results.
These settings are randomised each playthrough, so that {{backlog_topics[2]}} might be a promising topic in one playthrough, but disastrous in another.
Exploration through experimentation in order to figure out which topics to exploit is the core challenge in this game.
Effort
Each task requires a certain amount of effort to implement. There are 3 levels of complexity:
{{backlog_complexities[0]}},
{{backlog_complexities[1]}}, and
{{backlog_complexities[2]}}.
Increased complexity also results in higher risk or reward. Experiments that result from tasks of {{backlog_complexities[2]}} complexity tend to have larger effect sizes, in either direction, than those of {{backlog_complexities[0]}} complexity.
Experiments
Once a task is complete, a new experiment will be started. Each day, new users will be exposed to the experiment and statistics will be computed.
At any given moment, you can decide to either
or
an experiment.
Stopped experiments are removed from the game. Shipping an experiment will cause all future users to be exposed to this change.
You can review past experimental results and decisions in the history tab.
This is a game of chance, but not of chance alone. The faster you learn, the more you will earn. Good luck!
You shipped {{message.exp.name}}.
The real effect of this experiment was {{((message.exp.effect[1]-1)*100).toFixed(2)}}%.
You stopped {{message.exp.name}}.
The real effect of this experiment was {{((message.exp.effect[1]-1)*100).toFixed(2)}}%.
The game is over
{{feedback_levels[settings.feedback_level]}}{{settings.game_duration}} days
Your final score is {{format_percent(overall_performance.get_relative_effect(1)[0])}}.
This is significantly
betterworse
than would have been expected if there had been no product changes made at all.
This is not significantly different from what would have been expected if there had been no product changes made at all.
So You Think You Can Test?
Decision making under uncertainty is complicated business. This game aims to make decision makers more aware of the complex trade off between indecision and acting on insufficient information.
Simulation-mode
The amount of information you will have available during the simulation. Reduced feedback increases realism.
Longer play increases the opportunity to uncover the underlying simulation parameters through experimentation.
Leaderboard
{{feedback_levels[settings.feedback_level]}}{{settings.game_duration}} days