Stakeholder-Specific Vulnerability Categorization

MADS: Model Analysis & Decision Support

GenericCDSS is a web-based application, which provides the main dashboard where professionals (e.g, practitioners, nurses) can follow all the patients that are under their responsibility and some details about the state of each one.

An open source fullstack framework for interactive multiobjective optimization and decision-support..

Python scripts for downloading and analyzing iran bourse (stock exchange) data. اسکریپت پایتون برای دانلود و تحلیل داده های بورس تهران.

Demand Response Analysis Framework (DRAF)

ADRust: a tool in Rust to manage (Architecture) Decision Records

Streamline your ecoacoustic analysis with LEAVES, offering advanced tools for large-scale soundscape annotation and visualization. Join researchers and citizen scientists using LEAVES to analyze complex soundscapes faster and more accurately.

Bayesian Information Gap Decision Theory

A systems-thinking essay that explains why failure rarely happens suddenly. It shows how slow drift, accumulating pressure, and weakening buffers push systems toward collapse long before outcomes change, and why prediction-focused analytics miss the most important phase of failure.

A systems-thinking essay that reframes failure as a gradual transition rather than a discrete outcome. It explains how pressure accumulation, weakening buffers, and hidden instability precede visible collapse, and why prediction-based models arrive too late to prevent failure in human-centered systems.

A long-form systems essay arguing that most metrics fail because they measure outcomes instead of accumulated pressure. It reframes collapse as a consequence of debt, buffer depletion, and delayed feedback, and explains why early warning depends on measuring pressure rather than predicting final events.

An analytical essay on why prediction-based models fail in reflexive, unstable systems. This article argues that accuracy collapses when models influence behavior, and proposes equilibrium and force-based modeling as a more robust framework for understanding pressure, instability, and transitions in AI-shaped systems.

An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

A library of published compartmental epidemic models, and classes to represent demographic structure, non-pharmaceutical interventions, and vaccination regimes, to compose epidemic scenarios.

An early-warning system that models disasters as instability transitions rather than isolated events. It combines force-based instability modeling with an interpretable ML escalation-risk layer to detect when hazards become disasters due to exposure growth, response delays, and buffer collapse.

A long-form systems essay arguing that machine learning fails when used as an automated decision-maker in unstable environments. It reframes ML as an early-warning instrument that exposes pressure, instability, and shrinking intervention windows, preserving human judgment instead of replacing it with late, brittle decisions.

An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.

AgroMo is an Integrated Assessment and Modelling software that integrates 4M a CERES based crop model, the Biome-BGCMuSo biogeochemical and a simple agro-economical model in order to support decision makers at multiple scales.

R package for statistical and spatiotemporal modeling of epidemiological data for vector-borne diseases in Colombia