The unique selling proposition of Wings is that users contributing their forecasts in the community are rewarded when their predictions “answer” are closest to what really took place.
WINGS combine several concepts, ranging from attention rewards, forecasting markets, smart contracts, governance models and federated funds security. The platform places an emphasis on encouraging the WINGS community to identify and promote high-value proposals that have higher chances of positive financial returns. WINGS create a decentralized forecasting ecosystem that gives tangible incentives for WINGS token holders to put the effort in making the best available choices to maximize their rewards.
If you combine the three general business models of Google -1.09% , Kickstarter, and the DAO you might come close to what Wings is attempting to create.
Problems WINGS Strives to Achieve
The WINGS platform strives to solve the issue of “inappropriate” and “unworthy” project submissions and in the process, increase the probability that “future backers” which have the highest rewards with the lowest risk. WINGS will use the following technologies to achieve their end-result.
Swarm intelligence through decentralized forecasting market:
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
Flexible governance and participation models
WINGS employ a liquid DAO governance model where both the voter and delegate have a vote on any issue.
The participation model is creating in a way to encourage participation depending on the level of forecasting one contributes.
Those participating in forecasts on the WINGS platform receive a public rating representing their ability to make correct forecasts. In WINGS forecast ratings influences the reward that both the token owner and his delegate receive. A higher rating signals a participant who outperforms and therefor other token owners should be willing to loan their forecasting right to the particular participant, creating a meritocratic decision-making system. A participant’s forecast rating increases or decreases following forecasting rounds, and also according to the token holder’s forecast activity. The more accurate a forecast, the more the rating will increase, and similarly the more inaccurate a forecast is, the more the rating will decrease. There is an equilibrium point for each forecast where the rating will not change at all.
Deep learning and machine-based predictive modeling:
Deep Learning LSTM (Long-Short Term Memory) Networks, it would be possible to initiate human interaction with the bot and enable users to naturally converse with the system. Deep learning (also known as deep structured learning or hierarchical learning) is the use of artificial neural networks that contain more than one hidden layer.