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For each interaction we randomly select two agents i, j and compare their opinions.
Consider two neighboring agents i and j with opinions as x i and x j, respectively.
There exists an edge ( i, j ) ∈ E between agents i and j if they can directly communicate with each other.
If agents i and j are connected through a communication line, α ij ≠ 0, otherwise, α ij = 0.
TB-ADM is well suited for decentralized computation since the updates require only communication between agents i and j, who are one-hop neighbors.
Practically, suppose that two agents i and j are respectively in state (mathbf{x}_{i}^{(t)}) and (mathbf{x}_{j}^{(t)}) at time t.
In practice, this means that when agents i and j interact, the probability that j changes its self-appreciation depends on agent i's self-appreciation, through (p_{ij}), which determines whether or not an initiated event leads to any change.
In a trial where N patients were treated with the two agents i and j, we expected n responses with a binomial probability distribution P n | N, q ij = N n q ij n 1 − q ij N − n.
Yes, it's our beloved estate agents I am talking about.
"We signed three high-profile free agents," I continued.
Similar(1)
There do not exist agents (i,jin [0,1]) such that agent i's equilibrium expected utility is monotone increasing in enforcement expenditure c (fine f) whereas agent j's equilibrium expected utility is monotone decreasing in enforcement expenditure c (fine f).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com