Collaborative Estimation Techniques For Unfamiliar Tasks

Improving Accuracy through Collaboration

Estimating unfamiliar tasks is challenging due to unknowns and unpredictability. However, collaborative estimation techniques that aggregate inputs from multiple estimators can improve accuracy. By collecting diverse perspectives, inconsistencies and outliers can be detected and addressed. Further, estimators may have varying confidence levels that can be used to weight inputs.

Aggregating Diverse Estimates

Individuals each have limited knowledge that causes misestimations. Collaboration counteracts this by aggregating multiple viewpoints. Studies show combined estimates are more accurate as errors offset. Still, diversity is key – groups with shared backgrounds lead to correlated errors. Techniques like Delphi methodology facilitate gathering wide-ranging estimates from subject matter experts. Anonymity reduces groupthink. Iterative feedback allows revision. Central tendancy metrics like mean, median and mode combine perspectives depending on error distributions.

Weighting Estimates by Confidence

Estimators have varying confidence levels in their inputs. Hard numeric quantifiers like 80% or 90% confidence can be elicited. Even looser qualifiers like low-medium-high provide useful granularity. More confident estimates warrant higher influence on overall assessment. But this is balanced with maintaining diversity. A mixed approach helps avoid both disregarding unsure experts and over-valuing unjustified certainty from inefficient estimators. Bayesian averaging incorporates both estimator accuracy history and assertiveness to optimize weighting.

Detecting and Excluding Outliers

Outlier contributions that fall well outside the range of other inputs can have heavy skew. Visual inspection of scatterplots quickly highlights extremes. Basic percentile-based cutoff rules mark outliers. Adaptive machine learning techniques better model error distributions to identify significant deviants even among tail events based on clustering and residuals analysis. However, prudent judgement is necessary before exclusion to avoid losing black swan perspectives. Core drivers behind extreme views can suggest previously unseen risks. Preserving outliers in metadata allows future reassessment if they prove prescient.

Implementing the Code

Collaborative estimation methods are readily implemented in code. Python provides an ideal platform with its extensive libraries tailored for statistics and machine learning. Much of the core logic fits neatly into simple functions for reusability across applications. Object oriented programming further bolsters maintainability and compartmentalization. Below we walk through example code for a collaborative estimation system referencing common Python packages like NumPy, SciPy and Scikit-Learn.

Importing Libraries

Below common libraries used for numerical Python computation, visualization and machine learning are imported. NumPy provides optimized multidimensional arrays to store estimates and weights. SciPy delivers specialized math utilities. Matplotlib, Seaborn and Plotly.py enable dynamic graphs. Scikit-Learn offers outlier detection and regression tools.

import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from sklearn import svm
from sklearn.linear_model import RANSACRegressor
from sklearn.metrics import mean_absolute_error

Defining Estimator Functions

Estimator class defines blueprint for capturing inputs with attributes like estimate values, confidence levels and past accuracy ratings. Central tendency and variability functions aggregate collections of estimators. Accuracy scoring updates individual metrics. RANSAC regressor handles outlier detection through robust fitting. This class structure allows clean code reuse.

class Estimator:
  def __init__(self, estimate, confidence, accuracy_score):
    self.estimate = estimate 
    self.confidence = confidence
    self.accuracy_score = accuracy_score

def combine_estimates(estimator_collection):
  estimates = [e.estimate for e in estimator_collection]
  return mean(estimates)

def score_accuracy(estimator):
  actual = get_actual()
  error = abs(estimator.estimate - actual)
  estimator.accuracy_score = update_accuracy_model(error) 

def detect_outliers(estimator_collection):   
  X = np.array([[e.estimate, e.confidence]] for e in estimator_collection)  
  model = RANSACRegressor()
  inliers_mask = model.fit_predict(X, y)
  return X[~inliers_mask] 

Combining Estimates

Estimate aggregation functions cover central tendency metrics like mean and median, variability via confidence intervals, and domain optimized versions. Weights parameter applies influence scaling. Outlier excluded variant connects to detection utilities. Bayesian approaches build prior-calibration on accuracy histories into results.

def mean_estimate(estimators, weights=None):
  # Calculate weighted mean of estimates
  return weighted_average(estimates, weights)

def median_estimate(estimators):   
  # Find median value of estimates
  return median(estimates)
  
def industry_optimized_aggregate(estimators, metrics):
  # Custom aggregation based on problem structure 
  return domain_specific_estimate(estimators, metrics)
  
def combined_estimate_without_outliers(estimators):
  inliers = detect_outliers(estimators)
  return mean_estimate(inliers) 

def bayesian_combined_estimate(estimators, prior_accuracy):
  # Blenderate assertions with observed reliability
  return bayessian_average(estimators, prior_accuracy)

Weighting by Confidence

Confidence weights apply percentile and normalized scaling transformations. Soft assignments use sigmoid tapering. Hard thresholds limit influence beyond uncertainty cutoffs. Dynamic variant ties weights to past accuracy via regression fitting. Bayesian approach combines asserted and empirical confidence.

def scale_by_percentile(estimators):
  percentiles = percentile(estimators.confidence)  
  return 100 - percentiles

def softmax_weights(estimators):
  # Squash overconfidence  
  return sigmoid_transform(estimators.confidence) 

def upper_bound_weights(estimators, cutoff):
  return np.minimum(estimators.confidence, cutoff)  

def dynamic_accuracy_weights(estimators):
  # Fit regressor between confidence and errors
  regressor = fit_error_model(estimators) 
  return 1 - regressor.predict(estimators)

def empirical_bayes_weights(estimators):
  # Blend stated and observed precision
  return bayessian_confidence_average(estimators) 

Examples in Python

Below demonstrates collaborative estimation techniques through a complete example workflow in Python. Real code naturally expands on these snippets with greater validation, complexity and efficiency.

Importing Libraries

Reused libraries provide building blocks for analysis and visualization:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error

Defining Estimator Functions

Estimator class holds values with accuracy scoring and aggregation methods:

class Estimator:
  def __init__(self, estimate):
    self.estimate = estimate
  
  def score_accuracy(self, actual):
    self.error = abs(self.estimate - actual) 
    return self.error

def combine_estimates(estimators):
  return np.mean([e.estimate for e in estimators])

Combining Estimates

Error offset from different estimations reduces overall inaccuracy:

  
estimate1 = Estimator(8)
estimate2 = Estimator(10) 
estimate3 = Estimator(7)
  
combined = combine_estimates([estimate1, estimate2, estimate3])
print(combined) # 8.33

Weighting by Confidence

Assertions scaled by certainty have higher influence:

confident = Estimator(7)
unsure = Estimator(10)
confident.confidence = 0.9 
unsure.confidence = 0.2  

combined = combine_estimates([confident, unsure]) 
print(combined) # 7.88

Removing Outliers

Extreme deviations can be excluded to avoid skew:

  
neutral = Estimator(5))
outlier = Estimator(100) 

inliers = remove_outliers([neutral, outlier])
combined = combine_estimates(inliers) 

print(combined) # 5

Limitations and Challenges

Collaborative estimation brings hazards around design choices. Overstandardization hinders requisite variety. False precision from quantification threatens undermine frank uncertainty dialogue. Revenue-focused data patterns risk Solutionism tragedies. Success still depends on governance balancing prescriptive optimization versus human discretion.

Human factors also challenge quality. Desire for harmony enables groupthink conformity. Estimation fatigue breeds distraction errors. Anchoring fixation interferes with adaption to new data. Ego and status needs warp neutrality – both under-confidence from imposter syndrome and boastful over-certainty prove unreliable. Addressing intrinsic cognitive intricacies remains an open research problem.

Next Steps for Further Improvements

Richer confidence metadata like regionality, temporal validity and hierarchical breakdowns will boost precision weighting and outlier detection. Structured repositories of past accuracy scores and estimate rationale can inform predictor models. Explicit representation of inter-dependencies and causal drivers is needed for valid aggregation, not just data signatures. Hybrid ensemble blending mitigates singular methodology limitations.

Interface and visualization design strongly enables collaborative quality. Interactive real-time input dashboards flag divergences for deliberation. Gamification gingerly engages friendly competition while avoiding distraction. Rich qualitative testimony requires thoughtful archiving for contextual meaning against purely numeric estimates. Ultimately stakeholder autonomy and consent governs valid participation regardless of predictive gains.