Dynamic Payouts

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Foresure's payout model is designed to reward early and accurate predictions while ensuring solvency of the market.

Reward Distribution at Finality

Let the state $t=t_{end}$ be the finality time and resolution of true outcome $r\in \{1,2,\dots ,n\}$ is confirmed by Oracle. Trading halts at block $B(t_{\text{end}})$. Snapshot taken at block $B(t_{snap})<B(t_{\text{end}})$. Eligible addresses for the reward of holding true belief are those who are holding the true outcome tokens $q_r(B(t_{\text{snap}}))>0$ and did not sell tokens of any outcome after a lock period from $t_{snap}$ to $t_{\text{end}}$. Let $h$ be that holder address. Hence the total eligible rewarding tokens for all of the addresses can be calculated as

$$Q=\sum _{\mathrm{\forall }h}{q}_r(B(t_{\text{snap}})).$$

All losing outcome tokens $i\ne r$ expire worthless. The reward pool $R_p$ can be computed as,

$$R_p=\sigma (t)-Y_0-\sum L_p(t_{end})+\sum RRF.$$

Note that $R_p>0$ since there is no short selling or liquidity drain. Note that the reward pool depends on the total stables at liquidity reserve $\sigma (t)$ and accumulated Rewards Reserve Fee. Sum of all liquidity added by LPs and Initial pool creation Liquidity are returned back to corresponding providers. Note that being constant product invariant is used at the core for each outcome pool, there is the possiblity of sniping tokens earlier for less stables and later on when nearing finality trader might need to spend more stables to get same amount of tokens. Hence the new hybrid weighted reward distribution is proposed:

For each holder $h$, the normalized payout is

$$R_h=\frac{{\omega }_h}{\sum _u{\omega }_u}{R}_p,$$

where ${\omega }_h$ is the weight contribution by the token holder and it can be calculated as

$${\omega }_h=\phi \frac{U_h}{\sum _u{U}_u}+(1-\phi )\frac{H_h}{\sum _u{H}_u},$$

where $\frac{U_h}{\sum _u{U}_u}$ represents USD-weight to reward for the capital allocation and $\frac{H_h}{\sum _u{H}_u}$ represents eligible holders token weight which considers the early buyers. $\phi$ helps to preserver the proportionality and helps to mitigate the gaming the market.