Darrell Velegol’s research while affiliated with William Penn University and other places

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Publications (4)


Operationalizing the Kelly Method to Bet on an Innovation Project Portfolio
  • Article

February 2025

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5 Reads

Industrial & Engineering Chemistry Research

Darrell Velegol

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Narayan Ramesh

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Manish Talreja

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[...]

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Patrick Dudley

(a) Schematic showing the log bankroll y=ln(B/B0) increase by ρ to the right with a win (probability p) and decrease by λ to the left with a loss (probability q=1−p). In general, ρ≠λ. (b) Probability density ct(y) of Equation (6) after t=18 bets for parameters ρ=0.5, λ=0.8, and p=0.7. The discrete distribution (markers) spans from y=−tλ (all losses) to y=tρ (all wins) at regular intervals of ρ+λ. The distribution is annotated by the mean μt (vertical black line) and the standard deviation σt (horizontal black line). The continuum distribution c(y,t) (teal curve) provides an accurate approximation when σt≫ρ+λ or, equivalently, pqt≫1.
(a) Optimal betting fraction f as a function of time t for different quantiles γ obtained by the numerical solution of Equation (20). The parameters are p=0.5, a=1, and b=1.5 corresponding to the original game discussed in an earlier paper [6]. The continuum approximation is valid to the right of the vertical dashed line, t≫(pq)−1. (b) Distributions of the log bankroll y after t=20 bets for optimal betting fractions f corresponding to the first and third quartiles: γ=0.25 and γ=0.75. The parameters are those from part (a).
(a) Transient distribution c(y,t) for the log bankroll y for Péclet number, Pe=ℓU/D=1. The log bankroll is scaled by the ruin length ℓ; time is scaled by the diffusive time scale ℓ2/D. Markers at the absorbing boundary denote the transient ruin probability R. (b) Accumulated ruin probability R of Equation (27) as a function of time for different Péclet numbers Pe=ℓU/D>0. Time is scaled by the diffusive time scale ℓ2/D. The values asymptote for any given value of Pe, to the value given in Equation (28).
Optimal betting fraction f as a function of time t for different quantiles γ reproduced from Figure 2a for p=0.5, a=1, and b=1.5. The black curve shows the ruin boundary, above which the ruin probability is greater than a specified value Rmax=0.01. The ruin tolerance r=0.00969 is chosen to permit the Kelly solution fKC=1/6 at long times. For short times, one might prefer to use a “fractional Kelly Criterion” to increase safety.
Analyzing Sequential Betting with a Kelly-Inspired Convective-Diffusion Equation
  • Article
  • Full-text available

July 2024

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54 Reads

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1 Citation

The purpose of this article is to analyze a sequence of independent bets by modeling it with a convective-diffusion equation (CDE). The approach follows the derivation of the Kelly Criterion (i.e., with a binomial distribution for the numbers of wins and losses in a sequence of bets) and reframes it as a CDE in the limit of many bets. The use of the CDE clarifies the role of steady growth (characterized by a velocity U) and random fluctuations (characterized by a diffusion coefficient D) to predict a probability distribution for the remaining bankroll as a function of time. Whereas the Kelly Criterion selects the investment fraction that maximizes the median bankroll (0.50 quantile), we show that the CDE formulation can readily find an optimum betting fraction f for any quantile. We also consider the effects of “ruin” using an absorbing boundary condition, which describes the termination of the betting sequence when the bankroll becomes too small. We show that the probability of ruin can be expressed by a dimensionless Péclet number characterizing the relative rates of convection and diffusion. Finally, the fractional Kelly heuristic is analyzed to show how it impacts returns and ruin. The reframing of the Kelly approach with the CDE opens new possibilities to use known results from the chemico-physical literature to address sequential betting problems.

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Citations (1)


... An important one is the use of "fractional Kelly", in which one reduces risk by betting perhaps a half or a fourth of what the Kelly Criterion recommends [9]. Another is a Kelly Criterion with learning [10], which accounts for a change in parameters (e.g., a change in probability of winning) as expenditures are made. For example, as work is conducted on a project, the probability of success might rise. ...

Reference:

Analyzing Sequential Betting with a Kelly-Inspired Convective-Diffusion Equation
Gambling on Innovation with Learning
  • Citing Article
  • December 2022

Industrial & Engineering Chemistry Research