Net Invested Score (NIS) Overview

• NIS is a continuous, reputation-weighted metric that aggregates expert forecasts using token allocations and dynamically updated investor ratings.
• It employs a fixed token budget with a two-bucket system to ensure broad expert participation and mitigate biases in research evaluations.
• Empirical simulations and theoretical guarantees demonstrate that NIS effectively surfaces high-impact papers while resisting manipulation.

Net Invested Score (NIS) is a continuous, reputation-weighted prestige metric for academic papers, developed as the central signaling mechanism in the Impact Market protocol. NIS is calculated in the “Investment” phase of the Impact Market: expert community members allocate fixed budgets of tokens to published papers, each token weighted by the investor’s dynamically updated Investor Rating (IR), thereby producing a public, immediate, and quantitative assessment of each work’s anticipated impact. This fully automatable system structurally replaces binary accept/reject outcomes with a transparent spectrum of peer-aggregated forecasted impact, with both theoretical guarantees and robust empirical results demonstrating its ability to surface high-impact research while defending against manipulation and bias (Sankaralingam, 16 Dec 2025).

1. Formal Definition and Calculation

The Net Invested Score for a paper $p$ is defined as

$$\text{NIS}_p = \sum_{i\in\mathcal{I}} (T_{i,p} \times IR_i)$$

where:

• $\mathcal{I}$ is the set of all qualified investors for the venue;
• $T_{i,p} \in \mathbb{R}_+$ is the number of tokens staked on paper $p$ by investor $i$;
• $IR_i \in \mathbb{R}_+$ is the Investor Rating of $i$ at the moment of investment.

Each token represents reputational currency, not monetary value, and the weighted sum accentuates the influence of forecasters with strong accuracy histories. IR is initialized to 1.0 for all newcomers and subsequently recalibrated based on each investor’s predictive track record in prior cycles, measured against realized impact via the Multi-Vector Impact Score (MVIS).

2. Phase 2 Investment Process

The computation of NIS occurs in the second phase of the Impact Market protocol, immediately following broad acceptance of all sound papers in Phase 1. The investment mechanics feature the following workflow:

• Token Budgets and Two-Bucket Allocation: Each investor receives a fixed, non-transferable token budget (e.g., +100 tokens), divided into:
  • Scrutiny Budget (e.g., 40 tokens): Must be distributed across 5–10 randomly assigned papers, with at least 1 token per paper, ensuring universal baseline expert coverage.
  • Expertise Budget (e.g., 60 tokens): Freely allocated by the investor, up to a cap per paper (e.g., 10 tokens), to reflect domain expertise and conviction.
• Investor Qualification and Set Membership: Investors are senior community members, commonly with ≥5 publications or program committee (PC) service; the set is fixed per venue to preclude Sybil attacks.
• Investment and Weighting: For each paper, an investor’s raw allocation $T_{i,p}$ is multiplied by the investor’s current $IR_i$ to compute the weighted contribution to $\text{NIS}_p$.
• Publication of Rankings: After aggregation, the resulting NIS values are reported both as raw scores and as percentiles, forming a continuous leaderboard of immediate prestige.

3. Interpretation of Components and Scenarios

Token Quantity ($T_{i,p}$) quantifies the investor’s staked conviction for a given paper. Investor Rating ($IR_i$) acts as a multiplier modulating each investor’s total influence, where $IR_i > 1$ amplifies and $IR_i < 1$ attenuates impact proportional to skill demonstrated in past predictions. The fixed investor pool, defined by community credentialing, serves as a Sybil-resistant backstop and ensures all participants are established experts.

NIS responds dynamically to concentration as well as consensus: a single high-IR investor’s substantial bet can outweigh a large crowd of lower-IR, lower-conviction bets. The protocol optionally admits negative tokens, allowing for explicit expression of expected underperformance; negative contributions reduce the aggregate NIS score for such works.

Term	Definition	Significance
$T_{i,p}$	Tokens staked by investor $i$ on $p$	Signifies degree of investor conviction
$IR_i$	Investor’s current rating	Encapsulates accuracy in long-term forecasts
$\mathcal{I}$	Set of all eligible investors	Ensures qualified, Sybil-resistant pool

4. Calibration, Feedback, and Iterative Updating

In Phase 3, three years after initial investment, the real-world impact of each paper is measured by MVIS, aggregating citations, software adoption, cross-disciplinary influence, and patent citations. Each investor’s performance is then evaluated: Phase 2 token allocations are interpreted as quantitative forecasts, and prediction accuracy is scored against realized MVIS outcomes. The IR for each investor is adjusted accordingly, subject to caps and decay factors, such that successful forecasters consistently gain influence, while those whose investments do not track to subsequent impact lose weight in the system. This mechanism drives a reputation feedback loop, continuously refining community-level forecasting and prestige signaling (Sankaralingam, 16 Dec 2025).

5. Theoretical Guarantees and Manipulation Resistance

NIS inherits several theoretically grounded properties:

• Convergence: In the limiting case where IRs perfectly reflect forecasting skill, NIS rankings will converge to the eventual ordering realized by MVIS, i.e., accurate investors dominate the signal, rendering NIS an unbiased forward-looking impact metric.
• Collusion Deterrence: Attempts at manipulation (e.g., collusive inflation of NIS) are rendered costly by IR recalibration. Artificially boosting a paper without corresponding long-term impact results in measurable IR loss for all colluders unless they can coordinate genuine “citation cartels” across all MVIS vectors—an infeasible, traceable, and resource-intensive effort.
• Attention Bottleneck Mitigation: The requirement that all investors allocate scrutiny tokens to randomly assigned papers ensures even niche or low-visibility works receive baseline expert investment, reducing risk of neglect and increasing discovery potential.
• Decoupled Skill Measures: Distinction between NIS (community-aggregated paper resonance) and IR (individual forecasting skill) enables separate identification of “great critics” (high IR) and “great artists” (high NIS), a separation not possible in standard peer review.

6. Empirical Validation via Simulation

Extensive agent-based simulations assess NIS performance relative to traditional binary gatekeeping. In scenarios characterized by low forecasting skill (mean IR ≈ 0.25), NIS matches the Current Protocol in gem (high-impact paper) recall at ≈28%. However, when allowing for investor agency and conviction betting, gem recall rises dramatically (≥86.7%) even in low-skill contexts, and approaches 99–100% under realistic or ideal skill distributions. These findings empirically substantiate that NIS, combined with self-selection and high-agency investment strategies, enables effective surfacing of high-impact work well beyond the capabilities of current review protocols (Sankaralingam, 16 Dec 2025).

7. Role in Credentialing, Feedback, and Community Impact

NIS serves as an immediate, quantitative prestige signal, providing actionable feedback for authors and the wider academic community. Its transparent, data-driven construction enables real-time assessment of community credence, while its integration into the Impact Market protocol harmonizes immediate credentialing with delayed verification, ultimately aligning reputational reward with forecasting accuracy. The NIS mechanism thus operationalizes a shift from subjective, hidden reviewer judgments toward an accountable and scalable system for scholarly recognition and advancement.