| | --- |
| | language: en |
| | license: apache-2.0 |
| | task_categories: |
| | - tabular-classification |
| | - text-classification |
| | - reinforcement-learning |
| | tags: |
| | - mixed-initiative |
| | - decision-theory |
| | - calibration |
| | - attention |
| | - interruption-management |
| | - hci |
| | - scheduling |
| | - synthetic-data |
| | pretty_name: "LookOut-inspired Mixed-Initiative Traces (Synthetic)" |
| | size_categories: |
| | - 10K<n<100K |
| | --- |
| | |
| | # LookOut-inspired Mixed-Initiative Traces (Synthetic) |
| |
|
| | This dataset provides **synthetic interaction traces** for studying **mixed-initiative decision making** under uncertainty, with **attention-sensitive interruption costs** and **decision-theoretic thresholds** inspired by ideas in [Eric Horvitz’s CHI’99 *LookOut* work](https://dl.acm.org/doi/10.1145/302979.303030). |
| |
|
| | **Source code:** |
| | https://github.com/DBbun/Mixed-Initiative-Lookout-CHI99 |
| |
|
| | **Developed by DBbun LLC — January 2026.** |
| |
|
| | **What this is (in one sentence):** |
| | A reproducible, decision-theoretic benchmark where each episode logs (i) calibrated uncertainty, (ii) attention state, (iii) the agent’s chosen mixed-initiative action, (iv) outcomes, and (v) analytic threshold explanations. |
| |
|
| | > This dataset and generator are inspired by publicly described concepts in the literature. |
| | > They are **not** affiliated with Microsoft Research or Eric Horvitz and do **not** include any original LookOut code or data. |
| |
|
| | --- |
| |
|
| | ## Files in this dataset |
| |
|
| | - **`mixed_initiative_traces.csv`** |
| | Main tabular dataset. Each row corresponds to one mixed-initiative decision episode. |
| |
|
| | - **`mixed_initiative_traces.jsonl`** |
| | Episode-level JSON Lines version with nested structure (context, beliefs, utilities, thresholds, outcomes). |
| |
|
| | - **`mixed_initiative_traces.quality_report.txt`** |
| | Calibration metrics (AUROC, ECE), monotonicity checks, baseline policy comparisons, and diagnostics. |
| | |
| | - **`mixed_initiative_traces.config.json`** |
| | Full generator configuration including random seed, utility matrices, attention model parameters, and constraints. |
| | |
| | --- |
| | |
| | ## Motivation |
| | |
| | Mixed-initiative assistants must decide whether to act immediately, ask for clarification, scope a safer action, or remain silent. These decisions depend on uncertainty, user attention and readiness, urgency, and asymmetric costs of errors. |
| | |
| | This dataset enables benchmarking of decision policies, calibrated uncertainty models, interpretability via analytic thresholds, and attention-aware action selection. |
| | |
| | --- |
| | |
| | ## Concept-to-data mapping (Figures 1–7) |
| | |
| | This dataset provides data-driven analogs to the CHI’99 LookOut concepts: |
| | |
| | - **Fig 1 — Manual invocation** |
| | Manual inspection and explicit invocation |
| | (`manual_hover_inspect`, `manual_click_invoke`) |
| | |
| | - **Fig 2 — Explicit social agent** |
| | Dialog confirmation and refinement |
| | (`dialog_confirmed`, `refine_after_accept`) |
| | |
| | - **Fig 3 — Automated scoping** |
| | Risk-reducing partial actions |
| | (`agent_action = scope`) |
| |
|
| | - **Fig 4 — Action vs no-action threshold** |
| | Expected utility comparison |
| |
|
| | - **Fig 5 — Context-dependent shifts** |
| | Threshold changes under attention and urgency |
| |
|
| | - **Fig 6 — Dialog as a second option** |
| | Three-way expected utility comparison |
| |
|
| | - **Fig 7 — Dwell time vs message length** |
| | Sigmoid attention model |
| | (`message_length_bytes`, `dwell_time_sec`) |
| |
|
| | --- |
| |
|
| | ## Data generation overview |
| |
|
| | Each episode is generated as follows: |
| |
|
| | 1. Sample latent evidence and compute calibrated posterior `p_true` |
| | 2. Apply belief distortion to obtain `p_model` |
| | 3. Sample urgency and compute attention/readiness |
| | 4. Compute expected utilities and analytic thresholds |
| | 5. Select action via expected-utility maximization |
| | 6. Sample user response and realized utility |
| |
|
| | --- |
| |
|
| | ## Recommended tasks |
| |
|
| | 1. **Calibration and uncertainty learning** |
| | 2. **Policy learning / imitation learning** |
| | 3. **Counterfactual decision analysis** |
| | 4. **Interpretability and explanation benchmarking** |
| |
|
| | --- |
| |
|
| | ## Schema (detailed column descriptions) |
| |
|
| | Each row represents a **single mixed-initiative decision episode**. |
| |
|
| | ### Identity |
| |
|
| | - **`episode_id`** |
| | Unique identifier for the episode. |
| | |
| | - **`user_id`** |
| | Synthetic user identifier used to simulate repeated interactions. |
| |
|
| | --- |
| |
|
| | ### Ground truth and uncertainty |
| |
|
| | - **`true_goal`** *(binary)* |
| | Whether the suggested action was actually appropriate. |
| | |
| | - **`p_true`** *(float, 0–1)* |
| | Calibrated posterior probability of correctness derived from latent evidence. |
| |
|
| | - **`p_model`** *(float, 0–1)* |
| | Agent’s reported belief after miscalibration. |
| | |
| | - **`evidence_score`** *(float)* |
| | Latent evidence signal used to derive `p_true`. |
| |
|
| | --- |
| |
|
| | ### Attention and readiness |
| |
|
| | - **`message_length_bytes`** *(int)* |
| | Size of the candidate interruption message. |
| |
|
| | - **`dwell_time_sec`** *(float)* |
| | Simulated dwell time before interruption. |
| |
|
| | - **`time_since_focus_sec`** *(float)* |
| | Time elapsed since last user engagement. |
| | |
| | - **`not_ready_score`** *(float, 0–1)* |
| | Aggregate interruption cost estimate. |
| | |
| | --- |
| | |
| | ### Context |
| | |
| | - **`urgency_0_1`** *(float, 0–1)* |
| | Task urgency affecting the cost of inaction. |
| | |
| | --- |
| | |
| | ### Mixed-initiative decision |
| | |
| | - **`lookout_modality`** *(categorical)* |
| | Interaction mode: `manual_invocation`, `explicit_agent`, or `auto_scoping`. |
| |
|
| | - **`agent_action`** *(categorical)* |
| | Chosen action: `no_action`, `action`, `dialog`, or `scope`. |
| | |
| | --- |
| | |
| | ### Manual invocation signals |
| | |
| | - **`manual_hover_inspect`** *(binary)* |
| | User inspected suggestion. |
| |
|
| | - **`manual_click_invoke`** *(binary)* |
| | User explicitly invoked suggestion. |
| |
|
| | --- |
| |
|
| | ### Dialog and refinement |
| |
|
| | - **`dialog_confirmed`** *(binary)* |
| | User accepted dialog suggestion. |
| | |
| | - **`refine_after_accept`** *(binary)* |
| | User requested refinement after acceptance. |
| | |
| | --- |
| | |
| | ### Outcomes and utilities |
| | |
| | - **`user_response`** *(categorical)* |
| | `accept`, `reject`, or `ignore`. |
| |
|
| | - **`realized_utility`** *(float)* |
| | Utility actually realized after outcome sampling. |
| | |
| | - **`u_baseline_no_action`** *(float)* |
| | Utility that would have occurred under no action. |
| |
|
| | --- |
| |
|
| | ### Expected utilities |
| |
|
| | These fields store the **expected utility values computed at decision time**, before sampling the user response. |
| | They are the quantities used by the policy to choose an action. |
| |
|
| | - **`eu_no_action`** *(float)* |
| | Expected utility of taking **no action**, balancing avoided interruption cost against delay or missed-opportunity cost. |
| |
|
| | - **`eu_action`** *(float)* |
| | Expected utility of taking the **primary action immediately**, combining expected benefit, error penalty, and interruption cost. |
| | |
| | - **`eu_dialog`** *(float)* |
| | Expected utility of initiating **dialog or clarification**, trading lower risk for a chance to improve correctness. |
| |
|
| | - **`eu_scope`** *(float)* |
| | Expected utility of taking a **scoped or partial action**, providing reduced downside risk with partial benefit. |
| | |
| | --- |
| | |
| | ### Analytic decision thresholds |
| | |
| | Each threshold represents the **minimum belief probability (`p_model`)** at which one action dominates another in **expected utility**. |
| | All thresholds are computed **analytically** from the utility model. |
| |
|
| | - **`p_star_noaction_action`** *(float, 0–1)* |
| | Threshold above which **action** is preferable to **no action**. |
| | |
| | - **`p_star_noaction_dialog`** *(float, 0–1)* |
| | Threshold above which **dialog** is preferable to **no action**. |
| |
|
| | - **`p_star_dialog_action`** *(float, 0–1)* |
| | Threshold above which **action** is preferable to **dialog**. |
| | |
| | - **`p_star_noaction_scope`** *(float, 0–1)* |
| | Threshold above which **scope** is preferable to **no action**. |
| |
|
| | - **`p_star_scope_action`** *(float, 0–1)* |
| | Threshold above which **action** is preferable to **scope**. |
| | |
| | - **`p_star_scope_dialog`** *(float, 0–1)* |
| | Threshold above which **dialog** is preferable to **scope**. |
| |
|
| | --- |
| |
|
| | ## Quality report |
| |
|
| | The file `mixed_initiative_traces.quality_report.txt` documents calibration results, sensitivity checks, and policy comparisons. |
| |
|
| | --- |
| |
|
| | ## Reproducibility |
| |
|
| | The dataset can be exactly regenerated using the source code and |
| | `mixed_initiative_traces.config.json`. |
| |
|
| | --- |
| |
|
| | ## License |
| |
|
| | Apache-2.0 |
| |
|
| | --- |
| |
|
| | ## Citation |
| |
|
| | If you use this dataset, please cite: |
| |
|
| | **LookOut-inspired Mixed-Initiative Traces (Synthetic)** |
| | DBbun LLC, January 2026. |
| |
|
