Cutting Edge Issues in Multilevel Theory and Research Part II Jeffrey B. Vancouver

Overview Describe the “dull edge” of multilevel theory and research Describe generic problem: complexities of dynamics Describe response: simulate computational models o Present a simple computation model as example and to highlight issues Mention some extensions o systems interacting across units o subsystems nested within a unit

Dull Edge: Limited Model Specifications Single and multilevel models specified in terms of causes (x’s) and effects (y’s) o Figure 1.1 in Kozlowski & Klein (2000) Yet, dynamic and feedback processes are a ubiquitous element of systems o y t = F(y t-1 ) o Definitional for Bertalanffy’s (1972) GST That is, effects become… o causes o depend on current levels

Generic Problem: Flow of Information Environment (opportunities, targets, options, etc.) Person (needs; wants; goals) ActionsPerceptions Own, internal dynamics thoughts/automatic processes 4

Superficially Recognized  Multilevel modeling, where observations are nested within units over time (e.g., experience sampling), is used to exam these processes  However, the complexities of the dynamics need greater attention to …  Understand phenomena  know what designs/data will test understanding

The Dangers of Dynamics Will consider simple goal-striving example o (person-level, could be other level) Will examine effect of hypothetical remedy for discrepancy o e.g., Smiling to placate angry customer Will consider two action types o all or none: furnace is on or off o degree of discrepancy: effort proportional to discrepancy from goal Will include emotion and fatigue

Simple Computation Model note: the higher the gain, the faster the effect of actions (velocity) (Goal)

Implications Effective remedy appears to backfire o Action/remedy use negatively related to current state across time o Compensating for negative effects of disturbance o Only used when needed o Even stronger negative effect if degree of discrepancy determines degree of output Emotion and fatigue likely by-product of discrepancy and discrepancy-reducing action, respectively o not necessarily directly involved in determining action o but may affect rate of action Depending on relative rates, type of action, and source of emotions, different effects may be observed

Extensions Example simple o single goal-striving system Imagine more complex processes o e.g., goal-striving systems across units Vancouver, Tamanini, & Yoder (2010) highlights interpretation difficulties given methods/designs used

Extensions Goal-striving systems nested within a unit o Learning o Thinking/Deciding o Acting Vancouver, Weinhardt, & Vigo (2014) o Highlights conceptual parsimony with comprehensiveness comparator output function input function desired perception (p*) rate (r) self-regulatory agent (subsystem) variable (v) disturbances (D) perception (p) discrepancy (d) output (o) stimuli (s) gain (k)

time comparat or time output time input expectancy A comparator expectancy A output expectancy A input expectancy B comparator expectancy B input expectancy B output time gain task choice comparator task choice output task choice input task A comparator task A output task A input task A goal task B comparator task B input task B output task B goal schedule comparator schedule output schedule input completed schedule Schedule Status Task A state Task B state incentive sensitivity task A gain task B gain time initial schedule done task A incentive task B incentive task A disturbance task B disturbance expectancy A agent expectancy B agent task A agent task B agent task choice agent schedule agent deadline time agent selected task lag comparator lag output predicte d lag supervisory signal Learning agent expected lag learning gain Emotion bias initial expected lag disturb comparator disturb output predicte d task state supervisory signal Learning agent expected disturbance initial expected disturbance learning rate expected disturbance weight Person Environment ∆ rate perceptio n of lag schedulin g rate memor y of task states task states Initial task states time step

Conclusion Relying on one’s mind to work out dynamic theory and its implications likely to mislead Examining process requires representing our models computationally to understand what data and theory could or should look like

Computational Modeling Conference/Workshop Ohio University ConferenceWorkshop o October 23, 2015October Learn about o Computational modeling/simulations generally o System dynamics or agent-based modeling specifically

action: all or none relation to state: negative

action: degree of discrepancy relation to state: highly negative

action: all or none gain and emotion negatively related

action: degree of discrepancy gain and emotion positively related