We’re getting machines to learn game theory,
creating the first general incentive model.
The model is designed to make sure that collaborators
are rewarded, and spammers are punished.
This model takes into account dozens of parameters such as: The position in the referral chain, relative result delivery-time, a referrer’s past references in this domain and, most importantly - each participant’s calculated ‘reputation score’. The reputation score is dynamic, reflecting the productivity and relevance of each participant’s link-sharing activity over time.
Participants with high conversion rates will gradually improve their reputation scores, while those sharing irrelevant links or spamming, will rapidly lose reputation points. When a link achieves a result, each participant will receive a percentage of the reward relative to their reputation score, strongly incentivising participants to target the links they share wisely.
In this model, the link creator is presented with a several options, to manually pre-set the distribution of rewards for achieved results. The options will include: dividing the rewards equally, compounding the reward over each new participant, emphasizing rewards to the last two collaborators, and many more distribution models to precisely fit your needs.
The model chosen by the contractor will then be embedded into the campaign contract, and will remain set throughout the campaign.
Obviously, this is best suited for link creators looking for surgical control over their ROI and reward distribution.
Choosing this model will allow each participant in the campaign to decide how much of the reward they wish to keep for themselves, and how much they choose to pass on to others who may help them achieve results.
Inherently, participants will need collaboration to achieve their goals.
So they’ll have to wager with the amount they choose to keep to themselves. That’s why we’ve developed a humorous UI for this model,
which graphically illustrates to participants a cardinal rule: the more you choose to keep for yourself, the lower the chances that you'll achieve results. We’re already curious to get some insights into game-theory from this fun model!