Tools that optimize
Choose the best feasible allocation, policy or portfolio.
118 of 388 tools.
Allocate attention budget
Use exact knapsack optimization to allocate limited expert-review time by expected avoided loss.
Allocate budget with CVAR constraint
Maximize expected portfolio return while keeping probability-weighted loss CVaR below a finance-owned tail-risk ceiling across aligned joint scenarios.
Allocate capacity by marginal value
Allocate indivisible aggregate capacity across initiative-specific diminishing marginal-value scenario curves, activation thresholds, hard minimum commitments, unit cost, and portfolio CVaR with discrete next-unit value and explicit solver certainty.
Allocate capacity nash bargaining
Allocate discrete shared capacity by weighted Nash social welfare over concave team utility curves, with disagreement guarantees and a utilitarian counterfactual.
Allocate restless bandit interventions
Allocate scarce recurring interventions across evolving Markov units with Whittle indices, explicit indexability checks, and paired policy simulation.
Design balanced stepped wedge rollout
Assign teams or other aggregate clusters to capacity-constrained rollout waves with pair-exchange optimization of cumulative causal balance, represented population, and rollout risk.
Design incentive compatible metric contract
Design metric weights and audit rates as a robust Stackelberg contract, anticipating effort, gaming, detection, guardrail harm, and adversarial equilibrium tie-breaking.
Optimize adaptive analytics plan
Choose an exact adaptive sequence of analyses and an outcome-contingent terminal action by Bayesian belief-state dynamic programming, allowing early stopping while enforcing cost, duration, dependency, exclusion and analysis-step constraints and measuring value over the best fixed analysis sequence.
Optimize agentic autonomy portfolio
Choose one manual, approval-required, bounded-autonomous or autonomous operating mode per action class, maximizing scenario net value under hard authorization/reversibility controls, shared-asset loss, reviewer capacity, cost, availability, dependencies and CVaR.
Optimize AI capability resilience portfolio
Choose unaided drills, work rotations, cross-training, dual running, fallback redesign or monitoring per aggregate capability class using exact binomial shortfall, common-provider unique loss, hard readiness/control/capacity gates and a CVaR Pareto frontier.
Optimize AI code assurance portfolio
Choose standard, expert, pair, property, formal or canary assurance per aggregate AI-code change stratum using Beta-binomial defect simulation, unique shared-component loss, hard controls/resources and a CVaR Pareto frontier.
Optimize AI compliance control portfolio
Select reusable AI compliance controls and one plan per obligation using Beta-binomial residual risk, shared jurisdiction loss, exact shared costs/resources and a CVaR Pareto frontier.
Optimize AI configuration rollout portfolio
Select one current or staged rollout plan per AI configuration release, maximizing expected value minus CVaR regret under hard controls, failure ceilings, application concurrency, dependencies, budget and shared resources while computing overlapping application blast-radius loss once from joint survival.
Optimize AI data rights remediation portfolio
Choose license, replace, delete, disable or retrain actions that maximize preserved risk-adjusted AI value under budget, legal/execution gates, dependencies and scarce resources, while pricing scenario CVaR and counting shared lineage contamination once at its weakest residual member.
Optimize AI evaluation value of information portfolio
Select additional AI evaluation plans by multi-stratum posterior-predictive value of sample information, discounting duplicate content and optimizing budget, reviewer capacity, delay, quality lower bounds and expected/CVaR incremental false-deployment loss with exact or disclosed beam search.
Optimize AI inference efficiency portfolio
Choose one governed AI inference efficiency design per workload across semantic caching, retry prevention, batching and unit reduction, maximizing risk-adjusted economic value under quality, latency, scenario availability, shared capacity, dependency, budget and CVaR-regret constraints.
Optimize AI knowledge refresh portfolio
Select one governed periodic refresh policy per unique knowledge source across every dependent AI application, using renewal-theory freshness, shared-source economics, hard access/control/grounding/loss/resource gates and expected plus CVaR scenario regret.
Optimize AI model routing portfolio
Choose one evidenced AI-model route per workload on a value/CVaR Pareto frontier under hard privacy, residency, retention, quality, latency, endpoint-capacity, route-availability, provider-diversity, concentration, budget and dependency constraints.
Optimize AI output IP risk portfolio
Choose keep, scan, license, redesign, replace, exclude or insure policies per aggregate AI-output class using Beta-binomial claim simulation, collectible indemnity, unique provider loss, hard controls/resources and a CVaR Pareto frontier.
Optimize AI privacy utility portfolio
Select one validated privacy mechanism per AI workload to maximize expected value minus privacy-loss CVaR while enforcing exact shared-account RDP composition, utility, latency, controls, dependencies, exclusions, budget and scarce privacy-engineering capacity, with shared compromise priced once.
Optimize AI workflow design portfolio
Select one governed AI workflow graph per use case, maximizing risk-adjusted business value under hard control, success, latency and scenario-availability gates plus shared model/tool/review capacity, dependencies, implementation budget and economic-regret CVaR.
Optimize alert decision threshold
Choose a cost-sensitive alert action threshold using cross-validated decision curves and bootstrap net-benefit evidence against constant policies.
Optimize analytics challenger portfolio
Optimize a budgeted portfolio of complementary analytical challengers over coherent common-mode failure scenarios, residual losses, stochastic review demand, dependencies, exclusions and tail-risk appetite, with exact subset enumeration or a disclosed dependency-closed greedy fallback.
Optimize attention aware alerting portfolio
Choose one governed alert policy per risk class with Erlang-C response queues and Monte Carlo risk, maximizing protected value net of missed/common loss, false-alert interruption, operating cost and CVaR under hard evidence, quality, response, budget, relation and capacity constraints.
Optimize board technology attention portfolio
Select a board technology-attention portfolio under agenda time, assurance budget, resource, mandatory-review, dependency and residual-risk gates while pricing Beta-binomial failures, shared strategic loss and CVaR.
Optimize budgeted initiative portfolio
Select the highest expected-value initiative portfolio under cash and multi-resource budgets while enforcing dependencies and mutual exclusions across aligned business scenarios.
Optimize calibration experiment portfolio
Choose which analytical functions to calibrate next with exact Beta-binomial posterior-predictive value of sample information, result-contingent activation thresholds, false-activation loss, experiment budget/capacity, dependencies, exclusions and exact-or-disclosed portfolio search.
Optimize capability transition network
Plan training and hiring as integral capacity flows through a time-expanded skill network, maximizing multi-period demand value net of transition cost and lead time.
Optimize carbon cost performance portfolio
Construct a dependency-, exclusion-, budget-, and capacity-feasible portfolio frontier across investment, expected and CVaR total cost including scenario carbon price, residual emissions, and performance capacity, with explicit interactions and exact-or-disclosed heuristic search.
Optimize causal release assurance portfolio
Select one release, assurance or hold option per change from prospectively identified Beta-binomial relative-risk effects while pricing delay, failure and shared common-mode loss under budget, scarce resources, mandatory controls, expected-failure and CVaR constraints with an exact or disclosed beam-search Pareto frontier.
Optimize CI assurance portfolio
Choose one baseline, cache, shard, test-selection, flaky-repair, mutation, integration-suite or runner-scale option per assurance unit using only prospective randomized/known-propensity fault-detection and feedback evidence, common random scenarios, controls, relations, budget, implementation and runner capacity, undetected-fault, latency and CVaR gates.
Optimize cloud reserved capacity
Choose an integer portfolio of dated cloud reservations across coherent demand, realization, spot, and on-demand scenarios; price unused commitment and unserved demand explicitly, enforce coverage and capital gates, optimize expected-plus-CVaR cost, and disclose exact versus deterministic supported-set search.
Optimize commercial commitment portfolio
Select decline or one executable contract-term package per commercial opportunity under common delivery scenarios, period capacity, delivery budget, expected penalty, acceptance-cash, liquidity and CVaR gates; value acceptance and relationship economics and disclose exact or uncertified beam search.
Optimize commercial resilience portfolio
Select a budgeted, capacity-feasible technical resilience portfolio directly on a deduplicated commercial exposure graph, combining simultaneous failures and multiple mitigations multiplicatively, enforcing CVaR and critical-loss gates, returning a cost-loss-tail Pareto frontier, and disclosing exact or deterministic beam search.
Optimize contingent technology financing policy
Optimize initial and observed-signal-contingent financing, restructuring or investment-response actions on a coherent cash/debt/EBITDA scenario tree; enforce nonanticipativity, dependencies, exclusions, node budgets/capacity, liquidity and leverage chance constraints, tail funding need, enterprise value and exact-or-disclosed beam search.
Optimize correlated experiment sequence
Sequence pure-learning experiments over correlated intervention effects using conjugate Gaussian updates, Gauss-Hermite lookahead, early stopping, and terminal deployment value.
Optimize cyber control portfolio
Find a budget-, capacity-, availability- and defense-depth-feasible cyber-control portfolio on a nonlinear attack-path graph, recomputing unique-asset expected loss and CVaR under dependencies, exclusions and multiplicative effects.
Optimize deadline recovery plan
Choose a budget-feasible deadline recovery plan over a dependency DAG using correlated triangular task durations, uncertain acceleration effects, common random numbers, probability-gain-per-cost search, and backward pruning.
Optimize decision authority queue policy
Optimize delegation and escalation by assigning one eligible authority option to each decision class while internalizing nonlinear Erlang-C waiting externalities across shared reviewer pools, wrong-decision and escalation loss, coherent demand scenarios, operating cost, utilization-breach probability and CVaR.
Optimize decision calendar
Schedule dependent strategic decisions as information arrives, balancing contingent action value, delay cost, portfolio tail risk, deadlines, precedence, and scarce decision capacity.
Optimize delivery to cash intervention policy
Choose at most one evidence-backed intervention for each aggregate delivery-ready, accepted or invoiced milestone segment; propagate sequential stage mass under shared scenarios and maximize expected collected-cash net value minus CVaR subject to budget, capacity, liquidity and cash-target gates, with exact or explicitly uncertified beam search.
Optimize discount policy
Optimize one aggregate discount option per commercial segment against scenario purchase, retention, service-cost and contribution economics; enforce expected discount spend, delivery capacity, cross-segment rate-gap and downside gates, compare with an explicit zero-discount baseline, and disclose exact or heuristic search.
Optimize distributionally robust action
Choose the action with the best worst-case expected value when scenario probabilities may vary inside a KL-divergence ambiguity set.
Optimize engineering observability portfolio
Exactly select the budget-feasible metric and integration subset maximizing multivariate Gaussian information, then require held-out information retention with bootstrap uncertainty.
Optimize enterprise technology capital plan
Optimize a two-stage enterprise technology portfolio that commits initial capital now and allocates follow-on capital only after observable signals; enforce non-anticipativity, dependencies, exclusions, signal-specific budget/capacity and eligibility, compare with the best one-shot portfolio, quantify option value and CVaR loss, and disclose solver certainty.
Optimize error budget portfolio
Choose dependency-safe reliability interventions under money and capacity constraints using posterior SLO-breach economics.
Optimize financing terms nash bargaining
Select financing terms through exact risk-adjusted Pareto and weighted Nash bargaining: evaluate full-cost founder and new-investor payoffs on identical exit scenarios, convert lower-tail payout into transparent certainty adjustments, enforce company cash, founder control, investor return, downside, evidence and individual-rationality constraints, remove dominated terms and maximize the weighted log product of surplus above governed disagreement values.
Optimize finops commitment portfolio distributionally robust
Select a complete FinOps commitment portfolio that minimizes worst-case expected cost when scenario probabilities may move within a governed total-variation ambiguity radius.
Optimize focus coordination policy portfolio
Choose aggregate async, meeting-batching, protected-focus or coordination policies using prospectively identified effects shrunk by design reliability, common scenarios, unique shared loss, Pareto search and hard budget, capacity, response, focus, timezone and CVaR gates.
Optimize forecast elicitation portfolio
Choose which independent human or model forecasts to obtain next by learning chronologically validated contextual directional skill, simulating conservative entropy reduction, pricing decision relevance and removing duplicated information under budget and source-capacity constraints.
Optimize fundraising attention policy
Allocate the current fundraising attention epoch with age-aware controlled Markov arm values and an exact multiple-choice capacity knapsack: compare action versus passive continuation through later stages, price founder distraction and action cost, expose a dynamic attention index, and disclose that independently relaxed future capacity is not a globally certified multi-period schedule.
Optimize global review assignment
Assign an entire review portfolio globally under expertise, conflict, capacity, urgency, quality, independence, and load-balance constraints.
Optimize identity assurance response portfolio
Choose one preauthorized identity-assurance response per aggregate account-risk case by maximizing simulated net access value minus security, false-positive, operating and CVaR costs under budget, capacity, control, availability and due-process constraints.
Optimize incident learning portfolio
Choose immediate remediation or a predeclared experiment-contingent action for each failure mode using Bayesian value of information, prospective test accuracy and causal remediation effects, common scenarios, shared-loss accounting, Pareto search and hard cost, capacity and tail-risk gates.
Optimize insurance retention
Select an insurance retention and limit by minimizing premium plus expected retained loss and a configurable CVaR tail-risk charge under an optional tail-cost constraint.
Optimize knowledge resilience portfolio
Choose one baseline, cross-training, paired-review, rotation, documentation or backup-owner posture per critical knowledge unit using prospectively identified transport-weighted Beta-binomial relative-failure effects, contributor-availability and common-loss scenarios, exact or disclosed beam search, mentor/learner capacity, budget, expected-failure, CVaR and Pareto constraints.
Optimize learning vs earning allocation
Solve the exact finite-horizon Beta–Bernoulli bandit for allocating scarce units between a known earning baseline and uncertain actions that earn and update their posterior.
Optimize license seat portfolio
Choose integer license packs across aggregate seat pools under coherent demand, on-demand price and capacity scenarios; explicitly price unused and unserved seats, enforce budget, coverage and CVaR gates, and disclose exact versus deterministic supported-set search.
Optimize model averaged joint outcome decision
Choose a governed aggregate engineering action across competing plausible Bayesian-network structures using pseudo-Bayesian out-of-time model weights, coherent joint outcome worlds, causal-identification mass, weighted CVaR, worst-model regret, decision stability and the expected value of resolving model uncertainty.
Optimize multi period calibration maintenance
Optimize a finite-horizon analytics maintenance schedule by propagating each function's healthy/degraded Markov belief under passive operation or recalibration, valuing healthy decisions and uncalibrated loss, enforcing period cash and specialist-capacity constraints, and disclosing exact state enumeration versus deterministic beam search.
Optimize multi period capital allocation
Allocate indivisible project funding schedules across every period budget while respecting dependencies, exclusions, uncertain terminal value, discounting, and a retain-capital baseline.
Optimize multi period growth budget saturation
Allocate aggregate growth capital across channels and periods on coherent common scenarios while preserving channel-specific Hill saturation and carryover state: search discrete spend schedules, propagate contribution and unrestricted cash, and maximize expected net incremental value minus CVaR shortfall subject to total/period budgets, liquidity and contribution-probability gates, with exact certification or disclosed deterministic beam search.
Optimize onboarding mentorship portfolio
Choose an interference-aware mentorship portfolio using only prospective controlled effects, reliability shrinkage, common autonomy scenarios, unique shared loss, mentor capacity, service gates, CVaR and exact-or-disclosed beam search.
Optimize org health intervention portfolio
Select an anti-Goodhart intervention portfolio using conservative causal lower bounds on real operating loss, never score movement, with design/transport/fidelity shrinkage, negative controls, interference, shared-loss, equity, resource and CVaR constraints.
Optimize organizational change mitigation portfolio
Choose a dependency-safe portfolio of documentation, cross-training, review redistribution, onboarding, staffing buffers, staged rollout, rollback or migration-support mitigations that minimizes change loss under nonlinear overlap, common risk, budget, scarce skills, recovery deadlines and CVaR.
Optimize post merger technology integration portfolio
Choose retain, bridge, migrate, integrate or retire for every target capability while pricing delayed synergy, retained standalone value, Beta-binomial failures, unique platform loss, resources, budget and CVaR.
Optimize preventive maintenance policy
Optimize preventive replacement or refactoring intervals with Bayesian-scenario Weibull renewal-reward economics and a worst-case cost penalty.
Optimize probabilistic roadmap commitment
Select the highest-value dependency-safe roadmap that satisfies a joint capacity commitment probability and optional tail-overtime limit.
Optimize product mix
Optimize one discrete quantity option per product across a shared cash budget and multiple capacity pools using aligned contribution scenarios, expected value, CVaR downside, and an explicit exact or heuristic solver boundary.
Optimize queue staffing SLA
Invert an Erlang-C queue across weighted demand scenarios to find the lowest expected-cost staffing level that satisfies a wait-time SLA.
Optimize queueing network capacity
Choose minimum-cost integer capacity additions across a routed open queueing network using traffic equations, M/M/c waits, and exact budget dynamic programming.
Optimize receivables intervention policy
Choose at most one evidence-backed action for each lawful aggregate receivable segment, propagate open/disputed payment and default mass period by period on coherent market/cash scenarios, and maximize expected collected-cash net value minus CVaR shortfall subject to intervention budget, capacity, relationship loss, liquidity and collection-probability gates, using exact enumeration or disclosed deterministic beam search.
Optimize regime contingent growth capital policy
Choose one action for each observable recurring-revenue regime and reuse it on every matching future, charging unique commitment resources once; evaluate every policy on coherent regime paths with multiplicative ARR, cash-burn and full action cost, then maximize expected terminal ARR value plus cash minus CVaR shortfall subject to liquidity, target-ARR, dependencies, exclusions, budget and capacity.
Optimize reliability investment frontier
Construct a dependency- and exclusion-feasible Pareto frontier across investment cost, expected residual loss, CVaR loss, and expected downtime under coherent scenarios and explicit pair interactions, then select the least-cost evaluated portfolio clearing governed reliability targets.
Optimize reserve follow on allocation
Solve a two-stage follow-on capital problem: choose how much reserve to hold now, then choose at most one funding tier per company conditional only on the signal partition genuinely observable later, with coherent scenario value, opportunity cost, CVaR, a reserve Pareto frontier, value of available information, and exact-or-disclosed supported-policy search.
Optimize retention interventions by principal strata
Estimate who an optional retention intervention can actually help—not merely who looks likely to leave—from randomized principal strata, then allocate scarce capacity by conservative net value under harmed-stratum sensitivity, budget and fairness constraints.
Optimize risk adjusted technology portfolio
Choose a dependency- and exclusion-feasible technology investment portfolio on an expected-value, cost, shared-loss CVaR and economic-capital frontier, maximizing net value after a finance-owned capital charge while enforcing budget, capital, tail-loss and RAROC hurdles with exact or disclosed beam search.
Optimize risk mitigation portfolio
Select a dependency- and exclusion-feasible mitigation portfolio that maximizes expected net loss avoided within budget and an optional residual-CVaR ceiling.
Optimize roadmap real options
Optimize continue, defer, abandon and expand decisions across staged initiatives using Bellman recursion and current capital rationing.
Optimize roadmap under resource substitution
Choose a value-maximizing roadmap and one explicitly validated native or substitute resource plan per initiative within all capability capacities.
Optimize robust intervention portfolio
Choose a dependency-safe action portfolio that balances expected and worst-case outcomes.
Optimize safe AI routing exploration portfolio
Allocate bounded production traffic to one safe challenger per AI workload by posterior-predictive knowledge gradient, maximizing net learning value under local quality/harm evidence, privacy, latency, provider diversity, shared endpoint capacity, exploration budget, provider concentration and regret CVaR constraints.
Optimize sample size by decision value
Choose a two-arm experiment sample size by Bayesian expected value of sample information after implementation economics, sampling cost, posterior adoption and harm gates, regret, and Monte Carlo recommendation precision.
Optimize selective human AI review policy
Choose one eligible automation or human-review policy per decision segment using a coherent-scenario multi-choice stochastic program over residual loss, complete cost and review hours; enforce complementarity evidence, scenario capacity-breach probability and residual-loss CVaR with exact enumeration or disclosed beam search.
Optimize sequence dependent roadmap
Optimize a dependency-feasible roadmap sequence under category setup time, execution duration and cost, aligned uncertain value, and exponential value decay, with bounded exact enumeration and visible heuristic fallback.
Optimize service continuity investment portfolio
Choose one production-exercised continuity posture per service-risk unit by maximizing retained business value minus direct/common interruption loss, full cost and CVaR under RTO, RPO, residual-risk, control, dependency, budget and resource constraints.
Optimize shadow AI governance portfolio
Choose block, broker, migrate, allow-with-controls or monitor policy for each aggregate shadow-AI service class, maximizing expected value minus loss CVaR under residual-exposure, detection, control, dependency, exclusion, budget and resource gates while pricing common provider value at risk once through joint survival.
Optimize shared assumption hedging portfolio
Choose a budgeted, capacity-feasible portfolio of validation, option, diversification or mitigation actions against shared business assumptions, combining repeated actions as diminishing remaining-gap closure while preserving cross-initiative reach, multi-premise complementarity, dependencies, exclusions, common scenario costs, positive-value probability and CVaR.
Optimize shared platform investment
Choose a shared-platform option and adopter coalition under budget, capacity, joint scenarios, pairwise network value, CVaR, and individually rational Shapley-informed cost allocation.
Optimize software supply chain remediation portfolio
Choose exactly one accept, patch, upgrade, replace, isolate or remove option per governed component while unique application disruption paths, direct incident loss, transition/operating/upfront cost, license compliance, scenario availability, cross-option feasibility, budget, capacity and CVaR are optimized together with exact or disclosed beam search.
Optimize sovereign data placement portfolio
Choose one executable regional placement per governed data workload by Monte Carlo posterior risk and exact/beam Pareto search under hard residency, KMS, encryption, diversity, latency, availability, relation, budget and capacity constraints.
Optimize stage gate funding
Value project continuation and abandonment by backward induction at each evidence gate, then select a portfolio within initial and expected follow-on capital limits.
Optimize stochastic flow control MPC
Optimize the next delivery-flow control with stochastic receding-horizon model-predictive control, serial queue dynamics, calibrated arrival and capacity scenarios, expected/CVaR cost, switching limits, exact sequence search, and a disclosed beam-search fallback.
Optimize stratified evidence sampling
Allocate a fixed evidence budget across finite-population strata with exact discrete Neyman allocation and quantify precision gained over proportional sampling.
Optimize tail risk budget allocation
Allocate a finite mitigation budget across mutually exclusive component mitigation levels to minimize portfolio CVaR while preserving aligned scenario dependence.
Optimize team topology
Partition the collaboration graph into bounded teams while balancing preserved working relationships, skill coverage, membership stability, and fixed assignments.
Optimize technical asset lifecycle portfolio
Choose exactly one retain, modernize, migrate or retire option per technical asset under common scenarios, unique value-stream capability coverage, full lifecycle cost and obsolescence loss, cross-option feasibility, budget/capacity and expected-loss/CVaR gates; return a cost-value-tail Pareto frontier with exact or optimistic-bound beam-search disclosure.
Optimize technical debt paydown portfolio
Choose a dependency- and exclusion-safe technical-debt portfolio under capacity and cash budgets by discounting compounding recurring drag, failure exposure, remediation effectiveness, risk reduction, and engineering opportunity cost.
Optimize technology risk limit allocation
Allocate scarce aggregate technology risk limits across discrete locally executable operating envelopes, preserving option relations and common loss once; maximize expected net value after a capital charge subject to nominal, expected-loss, CVaR, economic-capital and RAROC appetite, then reconcile selected unit capital with exact or seeded Shapley allocation.
Optimize time consistent capital policy
Optimize a finite multistage capital policy that can actually be followed: attach action bundles to observable scenario-tree nodes, enforce local budgets/capacity plus pathwise dependencies and exclusions, roll scenario cash and terminal enterprise value, constrain liquidity chance and recursively nested conditional CVaR, and disclose exact global enumeration or deterministic beam fallback.
Optimize value realization recovery portfolio
Choose a budgeted, capacity-feasible portfolio of stage-specific value-recovery interventions under coherent scenarios, combining each action as diminishing closure of its remaining gap while preserving cross-stage strategic complementarity, dependencies, exclusions, positive-value probability, CVaR and exact-or-disclosed beam search.
Optimize vendor contract terms
Optimize vendor contract terms across coherent usage, service-credit, exit, and fallback-price scenarios using exact option evaluation, CVaR, Pareto screening, and total-variation probability robustness.
Optimize workforce policy tree
Optimize staged team/role capacity actions through uncertain demand by Monte Carlo backward induction.
Rank experiments by expected information gain
Rank prospective experiments by Bayesian mutual information and decision-aware expected value of sample information across explicit hypotheses, result likelihoods and decision payoffs; price usability, monetary cost and decision delay, expose recommendation-change probability, and preserve a value-information-cost-delay Pareto set.
Rank features by evidence adjusted ROI
Rank feature investments by reliability-shrunk ROI, downside probability, and CVaR using aligned outcome scenarios and an explicit skeptical prior.
Rank initiatives evidence adjusted value
Rank initiatives using an explicit mixture of finance-approved value scenarios and a skeptical prior weighted by backtested evidence reliability, with downside and CVaR gates.
Rank management actions
Rank reversible, evidence-backed management actions and separate blocked work.
Rank portfolio companies by risk adjusted progress
Rank stage-comparable portfolio companies by evidence-shrunk milestone value minus a CVaR downside penalty per cash consumed, with weak evidence explicitly unranked.
Recommend safe contextual bandit action
Recommend contextual aggregate interventions with Bayesian reward learning only inside a posterior logistic harm constraint, explicitly falling back to a governed baseline when no arm is safe enough.
Recommend stop continue scale decisions
Recommend stop, continue learning, or scale for aggregate initiatives using beta-binomial posterior rollout economics, independent harm gates, simulation precision, sampling cost, and opportunity decay.
Solve bayesian influence diagram
Solve an exact discrete Bayesian influence diagram over actions, chance-node DAGs, action-dependent conditional probabilities, pre-decision evidence, and additive utility tables, then quantify action regret and the expected value of perfect information for observable exogenous nodes.
Solve belief state management policy
Solve a finite-horizon partially observable management problem over calibrated latent operating states and quantify the value of adaptive observation.
Solve budgeted bayesian experiment portfolio
Choose a budget- and resource-feasible portfolio of Bayesian experiments whose correlated observations can change multiple governed deployment decisions.
Solve distributionally robust markov policy
Solve a discounted Markov policy against simultaneous L1 transition-confidence sets derived from empirical state-action counts.
Solve distributionally robust product portfolio
Solve a budgeted product portfolio under ambiguity in scenario probabilities, acyclic dependencies, mutually exclusive choices, and governed pairwise cannibalization or synergy using a total-variation uncertainty set.
Solve entropic risk sensitive markov policy
Solve a finite-horizon Markov policy under exponential downside utility and compare it with the risk-neutral policy using paired Monte Carlo lower-tail CVaR.
Solve robust multiobjective portfolio
Solve a budgeted dependency-safe portfolio against both scenario-probability ambiguity and every vertex of a bounded stakeholder-preference simplex, using governed utility anchors and returning practically nondominated supported tradeoffs.
Solve robust policy across causal models
Choose an aggregate policy across competing interventional causal models and a bounded posterior credal set: derive model-by-action expected utility from outcome probabilities, compute exact lower/upper utility and adversarial model weights, minimize worst-case regret, expose model disagreement and value of perfect model information, and fail closed when any causal evidence gate fails.