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Andrew G. Ross

Publications and source records attributed to Andrew G. Ross.

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Beyond Consistent Scenarios: Deriving Indirect Influence, Transition Resistance, and Adjustment Dynamics

Assessments of structural change and economic transition dynamics, such as those arising in the energy transition, depend on internally consistent qualitative scenarios specifying the policy environment, technology mix, governance arrangements, and demand conditions. Cross-Impact Balance (CIB) analysis derives such socio-technical scenarios as fixed-point attractors of an expert-elicited interdependency network, supplying structural inputs upon which assessment models (including energy system optimisation, agent-based, and general equilibrium frameworks) can draw. Standard CIB, however, delivers only this equilibrium catalogue, leaving four structural questions unanswered: how much network-weighted effort a given transition requires; which components are the true system-wide levers once indirect influence chains are counted; in what sequence the system adjusts; and how the network at a given attractor responds to an external shock. This paper extends CIB through Linear Response Theory, exploiting a structural isomorphism between the CIB drift matrix and the Leontief input-output technology matrix. Four analytical objects are derived in closed form: the Type I cross-impact multiplier, which aggregates all direct and indirect influence chains; the perturbation budget, a network-weighted and directionally asymmetric measure of transition effort; the impulse response function, which traces descriptor adjustment sequences and feedback-induced overshoots; and the unit-impulse shock profile, which characterises attractor-specific network sensitivity and yields a direct measure of structural resilience and susceptibility. The framework is applied empirically to an energy-transition cross-impact matrix, yielding all four objects for five structural equilibria, and transfers to any domain in which pairwise influence scores encode structural interdependencies.

econ.GN

From transient shocks to unexpected outcomes: disruptive drivers in scenario pathways

Scenario pathways (e.g. for the energy transition) often use a single trajectory or a band. That is not sufficient when one needs to understand why outcomes differ and under what stress or uncertainty they arise. Doing so requires tracking disequilibrium along pathways, comparing runs across "worlds" or storylines, and surfacing outcomes that are unlikely under a central view but plausible when how factors interact is uncertain. Cross-Impact Balance (CIB) is a well-established method for generating pathways. This paper extends CIB to formalise and implement these dimensions in pathway runs, and defines four run types that respectively emphasise one-off shocks, extremes under alternative regimes, influence-structure uncertainty that widens over time, and exogenous shocks as a baseline for comparison. The approach is applied to a socio-technical decarbonisation pathway for illustration. Together, the extensions support stress-testing, comparison across storyline or regime assumptions, and exploration of rare or surprising futures, and help analysts distinguish results that are stable across those assumptions from those that depend on structural uncertainty about the influence table.

physics.app-ph

AI-Simulated Expert Panels for Socio-Technical Scenarios and Decision Guidance

Socio-technical scenarios for net-zero and other transformation pathways combine qualitative storylines with quantitative models, embedding them in plausible societal contexts for model assessment. Conventional scenario generation is resource-intensive, can be limited in internal consistency and diversity of expert and stakeholder perspectives, and is rarely stress-tested. This paper introduces a synthetic, AI-based expert panel to address these bottlenecks. An AI model first simulates domain experts who agree on descriptors, states, and their interactions. A probabilistic Cross-Impact Balance analysis then generates internally consistent pathways, using stochastic shocks to assess robustness and pathway diversity. An AI stakeholder panel uses multi-criteria decision analysis to select a preferred pathway; an AI expert panel translates it into model-ready quantitative inputs. Although scalable and applicable to any other country or region, the framework is applied to Germany's energy transition as a proof of concept, and offers an alternative and/or supplement to scenario generation. Furthermore, it enables Virtual AI-Led Decision Laboratories for exploratory policy stress-testing and provides an approach for rapid, structured expert elicitation and decision support in other domains.

cs.CY