Solutions to bias in the modern scientific process

There is wide recognition of inherent bias problems in the modern scientific process. I attended a good talk by Brian Nosek today discussing the challenges, barriers and solutions. Among the scientific process challenges are flexibility in analysis of data, selective reporting, ignoring nulls and lack of replication. Some of these problems can be traced to basic human behavior and psychology including perceived norms, motivated reasoning, minimal accountability, and people are just plain busy.

Nosek proposes that the solution to these problems is to show the work and share it early in the research cycle. Signals and incentives to make this behavior visible is necessary.

Towards that end here are a few efforts to address these problems:

Open Science Framework -- tools for sharing work across the research cycle
http://osf.io

Registered reports -- peer review after research design, before collection and analysis
http://cos.io/rr

Tracking switched outcomes in trials:
http://compare-trials.org/

AsPredicted:
https://aspredicted.org/

For computation and data science -- code sharing:
https://gist.github.com

Agent-Based Models and Social Network Analysis

Agent-based models (ABMs) are computational models consisting of heterogeneous agents programmed with decision-making heuristics and learning abilities. Interactions between these agents and their simulated environment result in adaptation and emergent behavior. The integration of ABMs and social network analysis provides the opportunity to experiment with the effects of social influence in individual decision-making and emergent social processes, but few ABMs include social networks.

A review of the literature does reveal a growing body of social network experimentation in ABMs:

Table: Social Networks in Agent Based Models