Dynamic Learning in Strategic Pricing Games

2020 ◽  
Author(s):  
Matt Stern ◽  
John R. Birge
Keyword(s):  
Dynamic Learning ◽  
Strategic Pricing ◽  
Pricing Games

Dynamic Learning Groups

ASHA Leader ◽  
2009 ◽  
Vol 14 (5) ◽  
pp. 2-2
Author(s):  
Larry Boles ◽  
Amy J. Hadley ◽  
Jeanne M. Johnson ◽  
Joan A. Luckhurst ◽  
Christine Krkovich
Keyword(s):  
Dynamic Learning ◽  
Learning Groups

Constructing and Evaluating a Validation Argument for a Next-Generation Alternate Assessment

2020 ◽  
Author(s):  
Amy K. Clark ◽  
Meagan Karvonen
Keyword(s):  
Data Collection ◽  
Assessment System ◽  
Alternate Assessment ◽  
Theory Of Action ◽  
Validity Evidence ◽  
Dynamic Learning ◽  
Alternate Assessments ◽  
Overall Evaluation ◽  
Computer Based ◽  
Achievement Standards

Alternate assessments based on alternate achievement standards (AA-AAS) have historically lacked broad validity evidence and an overall evaluation of the extent to which evidence supports intended uses of results. An expanding body of validation literature, the funding of two AA-AAS consortia, and advances in computer-based assessment have supported improvements in AA-AAS validation. This paper describes the validation approach used with the Dynamic Learning Maps® alternate assessment system, including development of the theory of action, claims, and interpretive argument; examples of evidence collected; and evaluation of the evidence in light of the maturity of the assessment system. We focus especially on claims and sources of evidence unique to AA-AAS and especially the Dynamic Learning Maps system design. We synthesize the evidence to evaluate the degree to which it supports the intended uses of assessment results for the targeted population. Considerations are presented for subsequent data collection efforts.

Strategic Pricing in Volatile Markets

2018 ◽  
Author(s):  
Sebastian Gryglewicz ◽  
Aaron Kolb
Keyword(s):  
Strategic Pricing

scholarly journals Stackelberg pricing games with congestion effects

2021 ◽  
Author(s):  
Tobias Harks ◽  
Anja Schedel
Keyword(s):  
Cost Function ◽  
Stackelberg Game ◽  
Pure Strategy ◽  
Closed Form Expression ◽  
Social Optimum ◽  
Service Capacity ◽  
Worst Case ◽  
Congestion Cost ◽  
Pricing Games ◽  
Congestion Effects

AbstractWe study a Stackelberg game with multiple leaders and a continuum of followers that are coupled via congestion effects. The followers’ problem constitutes a nonatomic congestion game, where a population of infinitesimal players is given and each player chooses a resource. Each resource has a linear cost function which depends on the congestion of this resource. The leaders of the Stackelberg game each control a resource and determine a price per unit as well as a service capacity for the resource influencing the slope of the linear congestion cost function. As our main result, we establish existence of pure-strategy Nash–Stackelberg equilibria for this multi-leader Stackelberg game. The existence result requires a completely new proof approach compared to previous approaches, since the leaders’ objective functions are discontinuous in our game. As a consequence, best responses of leaders do not always exist, and thus standard fixed-point arguments á la Kakutani (Duke Math J 8(3):457–458, 1941) are not directly applicable. We show that the game is C-secure (a concept introduced by Reny (Econometrica 67(5):1029–1056, 1999) and refined by McLennan et al. (Econometrica 79(5):1643–1664, 2011), which leads to the existence of an equilibrium. We furthermore show that the equilibrium is essentially unique, and analyze its efficiency compared to a social optimum. We prove that the worst-case quality is unbounded. For identical leaders, we derive a closed-form expression for the efficiency of the equilibrium.

scholarly journals Processing of novel food reveals payoff and rank-biased social learning in a wild primate

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Charlotte Canteloup ◽  
Mabia B. Cera ◽  
Brendan J. Barrett ◽  
Erica van de Waal
Keyword(s):  
Social Learning ◽  
Learning Strategies ◽  
Individual Learning ◽  
Diffusion Experiment ◽  
Vervet Monkeys ◽  
Dynamic Learning ◽  
Novel Food ◽  
Bias Attention ◽  
Higher Rank ◽  
Learning From Others

AbstractSocial learning—learning from others—is the basis for behavioural traditions. Different social learning strategies (SLS), where individuals biasedly learn behaviours based on their content or who demonstrates them, may increase an individual’s fitness and generate behavioural traditions. While SLS have been mostly studied in isolation, their interaction and the interplay between individual and social learning is less understood. We performed a field-based open diffusion experiment in a wild primate. We provided two groups of vervet monkeys with a novel food, unshelled peanuts, and documented how three different peanut opening techniques spread within the groups. We analysed data using hierarchical Bayesian dynamic learning models that explore the integration of multiple SLS with individual learning. We (1) report evidence of social learning compared to strictly individual learning, (2) show that vervets preferentially socially learn the technique that yields the highest observed payoff and (3) also bias attention toward individuals of higher rank. This shows that behavioural preferences can arise when individuals integrate social information about the efficiency of a behaviour alongside cues related to the rank of a demonstrator. When these preferences converge to the same behaviour in a group, they may result in stable behavioural traditions.

Building Learning Communities for Faculty

1998 ◽  
Vol 2 (6) ◽  
pp. 22-24
Author(s):  
Jodi H. Levine
Keyword(s):  
Faculty Development ◽  
Learning Communities ◽  
Learning Community ◽  
Colleges And Universities ◽  
Dynamic Learning ◽  
Temple University ◽  
Teaching Team ◽  
Daunting Challenge ◽  
The Way

As at most colleges and universities, when faculty at Temple University are asked to join with other faculty to teach a “learning communities” course, they are faced with the daunting challenge of changing the way they teach. To help them meet this challenge, Temple University engages in a number of faculty development activities, the goal of which is to have faculty come together in a dynamic learning community—a teaching team—in which they can work out the best approaches for involving students in their own learning.

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