Systems Shape Behavior: Designing AI Around the Outcomes We Want
AI affordances are not fixed. We research multi-agent deliberation, cognitive bias, and architectural design to build systems that actively improve how organizations decide and work.
What the research shows about AI, organizations, and how they interact.
These findings inform every engagement we run. See how they apply →
Sentimental Agents: Exploring Deliberation, Cognitive Biases, and Decision-making in LLM-based Multiagent Systems
Elizabeth A. Ondula · Daniele Orner · Nick Mumero Mwangi · Casandra Rusti
Fourth Workshop on Knowledge-infused Learning · IJCAI 2024 · Barcelona
When several AI agents evaluate the same candidate, what decides the answer? The paper tests whether the method of combining their opinions changes the outcome.
Three agents, each with a distinct Mental Model of Self, deliberate over ten candidates across five rounds. Sentiment analysis and a non-Bayesian update track how every opinion moves. Three aggregation protocols then combine the same opinions three different ways.
The aggregation rule changed the outcome. Six of ten candidates shifted position between Borda count and a conviction-weighted ranking, including the top choice. A third protocol placed seven candidates in one undifferentiated tier and recommended nobody.
Ten candidates, one simulation run. Six changed position.
Any system that pools several AI judgments contains a voting rule. Borda count treats every ranking as equally certain. The conviction-weighted protocol divides each final sentiment by how much that agent wavered, so a confident opinion outweighs a volatile one. Applied to the same conversation, the two rules hand you different names.
When a hiring committee convenes, someone says "let's vote" or "I want everyone comfortable with this." The aggregation is a decision people argue about out loud. Deploy a multi-agent system and that same decision sits in a default nobody experienced as a decision.
Read the full paper · openreview.net →If you use AI for evaluation, scoring, or selection in hiring, vendor review, or strategic prioritization, your system already contains a voting rule. Name that rule, then test it against one alternative on evidence you already have.
Build it into a Tool → · Make it a Product →
Three open questions shaping current work.
These are live problems we're investigating. We're always adding more.
When a multi-agent system is embedded in a workflow, a governance question follows: who is making the decision? It changes depending on whether agents are advisory, evaluative, or executive as well as on where humans engage.
Before you optimize AI-assisted decisions, you need to know who’s actually deciding. A Playbook makes that mapping explicit.
See the Playbook →The Artificial Board of Advisors concept asks this directly. An AI panel drawing from behavioral economics, systems thinking, and organizational psychology, may offer new, adversarial counsel where outside perspectives are hard to get.
This is the research behind our Playbook design. Better AI advisory structure produces better strategic decisions from the same leadership team.
See the Playbook →Role Diagnostics maps human-AI collaboration structures. Sometimes humans set the goal and AI executes; sometimes AI surfaces options and humans decide; sometimes both deliberate. Which arrangement wins depends on the problem.
This informs how we design every internal Tool. We configure human-AI interaction to the specific problem your team is solving.
See the Tool →Research findings, written for leaders deciding now.
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We take on a small number of applied R&D partnerships each year. Each effort produces a working solution and a rigorous account of what we found. If you have a question to explore in both a scientific and actionable manner, let us know.
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